Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan

Buildings represent significant opportunities to reduce energy consumption through reductions in waste, improvements in efficiency, and changes in operations. The characterization of energy saving opportunities in buildings can be a labor-intensive process and needs to be undertaken in a manner that balances investments in auditing effort and energy savings analysis with the likelihood of actual investment in action. This research examines an integrative approach to the characterization of energy savings opportunities in buildings through the preparation of students to conduct “building retuning” assessments as an embedded engaged scholarship activity in a college-level engineering course. The motivation of this approach is the simultaneous accumulation of hands-on experience in energy assessment and evaluation by students and added values for building owners and managers. This will lead to more informed investments in low cost and no-cost energy savings actions as well as increasing the likelihood of making investments in more detailed auditing services. The rationale and design for course objectives and learning modules are presented. A case study in which trial building walkthrough assessments were used to assess the confidence of students as value-adding contributors to commercial building energy auditing are also described, and the evaluation of the alignment of the approach within a large campus of a research university are presented. The relationship between this effort and emerging auditing practices and credentials for building energy efficiency professionals is also presented along with a set of shared resources available to colleges and universities seeking to adapt and build upon this approach on their respective campuses.

Project-based group work is frequently implemented in academia in pursuit of collaborative learning among students in the same class. The purpose of this study is to assess the impact of vertical integration of classes on student learning in construction education. Two classes—sophomores who are less knowledgeable and experienced and seniors who are more knowledgeable and experienced—were integrated using a service project, in which they built a small shed building. This study adopts three ways to assess the impact: an interview with the client of project, a student survey, and an open session of reflection. The first intends to evaluate overall success of the project based on the client’s satisfaction level whereas the second and third attempt to measure how much the integration helped students increase their knowledge and skills for managing a construction project. The assessment results show that the vertical integration of classes promotes collaborative learning by mutually benefitting students in each class. This paper presents the process of developing and implementing a vertically integrative service-learning project, discusses challenges associated with the process, and provides recommendations. This detailed process should interest construction educators who wish to implement similar learning activities.

In the construction industry, construction accounting is a form a project accounting that is used to monitor the cost of a construction project, which involves charging construction costs to the applicable contract. Construction accounting requires the utilization of revenue recognition rules for construction contracts in progress. As there is a need for continued training for employees in the construction industry, the faculty at California Polytechnic State University, San Luis Obispo developed the construction industry simulation (COINS) to assist in training students in the management of a construction company managing multiple projects simultaneously. It was designed to assist students better understand the construction accounting processes, construction cost management systems, job costs and job cost reporting, the WIP, and the most important elements of a contractor’s financial statements. COINS engages students in the decision making of the heavy civil construction and commercial building sector. This simulation has been used in the classroom and now is just available to construction firms to use with personnel. This paper describes the development and use of COINS simulation designed and developed to educate future and existing construction management personnel.

Changes arising from the addition, deletion, and/or modification of a project’s design frequently cause delays. Such changes may take many forms; however, owner-acknowledged change should receive more attention than other types of change, as it might present a risk to both owners and contractors. These changes, both individually and collectively, can have a ripple effect on what appears to be the unchanged scope of a project’s work plan, with varying impacts on project schedule. The efficient management of design changes requires a minimization of the time impacts of these changes. This paper presents a building information modeling (BIM)-based quantification model, developed to help design professionals quantify the ripple effect of contemplated design changes requested by owners after the completion of the design phase and before the commencement of construction. The developed model calculates the impact of changes on a project’s time frame. After analyzing the ripple effect of time, the model updates the project schedule accordingly. The developed model is expected to help owners and their agents make better decisions about approving or rejecting contemplated changes. The model has been applied to a case project to demonstrate its use and to illustrate its essential features.

This article relates the process used to create a graduate class in human resource management (HRM) for a construction management master’s program. In an effort to provide graduate students better company management skills, a HRM course was added to the curriculum which focused on the unique aspects of human resources (HR) in the construction industry. In preparation for this course, the author visited with 12 commercial construction companies around the United States. These meetings with industry professionals provided the basis and structure for the course materials and assignments. The use of industry professionals provided guidance and current real world experience that was incorporated into the classroom. The findings can be used as a model for others on what to include in construction HR graduate classes. The results of this project can also serve as a guide for utilizing industry connections in creating new courses and assignments in construction management curriculum.

In fall 2014, the University of Colorado Denver (UC Denver) launched a new, interdisciplinary program within the Department of Civil Engineering. The mission of the Construction Engineering and Management (CEM) program is to provide the skills necessary to prepare the next generation of construction engineers and be effective leaders and managers within the ever-evolving construction industry. Developing a new program provided unique opportunities. However, developing a truly interdisciplinary program in engineering meant that few models or templates existed. At UC Denver, a team of faculty and assessment experts met monthly to develop the program’s vision, learning-objectives, curriculum, and assessment plan. This research documents the preliminary outcomes and lessons learned from such activities. Specifically, industry experts were surveyed and students were interviewed after year one of the program. Primary take-aways include: to properly prepare students for professional practice in construction today requires customizable and interdisciplinary curricula; to accomplish such an objective, it is best to develop an assessment plan in parallel with the curriculum; finally, it is necessary to iterate during program and curriculum development. While the opportunity to develop a new and original program is rare, today many universities are looking to develop innovative interdisciplinary programs. As a result, findings from this research are relevant and informative to engineering departments seeking to improve existing or develop innovative and interdisciplinary programs or curricula.

There is often a claim that there is a disconnection between theory and practice, which raises questions on student’s readiness for employment and the extent that higher education responds effectively to the evolving needs of the construction industry. Industrial placements schemes can facilitate student learning from construction practice whereby students can reflect on their learning experience at the university. The interplay between traditional engineering curriculum and the context-based learning is important for securing the right skills and competencies among the graduates. This paper takes outset in the hypothesis that external effectiveness (industry readiness) is better in those modes of education that, at least in part, are based on the world of work itself, and several sources highlight the necessity of strong links between engineering education and industry. Hence, it becomes central for the program to facilitate the industrial placement effectively. How to do so is investigated through a large survey among student that has finalized an internship scheme. The sample size consists of entries. Guidance on how to facilitate internship most effectively pre, per and post scheme is outlined and discussed.

The use of building information modeling (BIM) is continuously growing over time in the architectural, engineering and construction (AEC) industry worldwide. Also, many governments have established BIM as a requirement for public construction projects. As a result, a demand for professionals with BIM education and competency is increasing. In order to satisfy current and future BIM professional demand, universities must integrate BIM into their undergraduate programs. The aim of this study is to determine the need and the best approach to incorporate BIM into the Civil Engineering undergraduate program at University of Puerto Rico, Mayagüez Campus (UPRM). Through an extensive literature review, several approaches to incorporate BIM into the curriculum were evaluated and industry requirements for BIM education were identified. A framework to incorporate BIM across the curriculum in existing UPRM courses was developed, taking into consideration what skills industries expect new professionals to have and the cognitive domain of Bloom’s Taxonomy learning objectives. The framework and recommendations intend to provide the Civil Engineering Department faculty members’ a guide to develop a BIM curriculum and satisfy the actual and future industry demand for new professionals with BIM skills.

Academia and the construction industry are linked by a strong collaborative relationship through research and development (R&D); both have expectations for outcomes and impacts as incentives to maintain this relationship. However, assessing outcomes and measuring impacts is often challenging. To address this challenge, an evaluation framework for assessing the impact of construction R&D on the construction industry and academia is proposed in this paper. This framework consists of a “logic model” and an “evaluation plan” to define and evaluate construction R&D impacts on both the construction industry and academia. The logic model helps to define the relationship between academia and the construction industry in terms of inputs, outputs, and outcomes and impacts; this relationship is expressed using “if-then” rules to relate the inputs to outputs, and the outputs to outcomes and impacts. The evaluation plan helps determine the fulfillment degree of the expected outcomes and impacts from the perspectives of both the construction industry and academia. The proposed evaluation framework will help quantify and assess the impact of construction R&D on the construction industry and on academia so that the inputs of both parties can be better used to deliver the outcomes each expects.

The engineering project team (EPT) depends on the skillful and experienced combination of professionals in order to complete one project. Hence, with the development of construction industry, knowledge heterogeneity continues to strengthen and increases the difficulties of team communication. However, there has been very little empirical research to test whether the influence of knowledge heterogeneity on team performance is mediated by facets of team communication. It is found that through team communication effectively in EPTs can maximum offset knowledge heterogeneity and promote team performance. This paper provides useful insights into how EPTs should encourage employees’ team communication behaviors or actives so as to reduce the negative effects of knowledge heterogeneity that will in turn enhance engineering project team performance and leading to benefits for the organization as a whole. The value of this paper is to point out that successful teams were characterized by higher levels of team communication ability when compared to their unsuccessful counterparts. Based on this study’s empirical findings, managerial implications for building up team work are provided.

Per the ASCE’s ethical code, civil engineers are expected to respect the infrastructure sustainable development needs of involved stakeholders while upholding the profession’s integrity. There is a great need to design civil engineering curriculum to prepare students to address the challenges of future ethical decision dilemmas. This paper presents the development and implementation of a group-based infrastructure development exercise where students apply life-cycle analysis principles to design and plan a toll road project under challenging ethical vignettes. The students were formed in groups to act as a private JV company to propose a toll highway design and life-cycle plan with highest benefit-cost ratio while maintaining a profitable private company. Also, the groups were challenged with five main ethical vignettes that tie the public and private financial viability to hard decisions related to social justice, law compliance, honest competition, and labor relations. Exercise results analysis revealed that students were inclined to utilize higher public benefits to justify taking varying levels of unethical project decisions. The proposed exercise supports civil engineers ethics training by challenging them to adhere to professional ethical standards in hard decision-making situations.

Despite the introduction and increase in collaborative project delivery methods in recent years, academia is lagging in preparing students for collaborative practice. Approximately 70% of faculty members teaching collaborative project delivery methods, such as design-build and integrated project delivery, lack first-hand industry experience with the project delivery methods. In an effort to improve the teaching of collaborative skills, the authors conducted a two-day interactive symposium for nearly eighty students in which the students actively engaged in exercises on topics outlined in the American Institute of Architects Integrated Project Delivery Guide: process, team formation, communication, compensation, risk, and agreements. Surveys were conducted at the outset and conclusion of the symposium to determine the efficacy of the interactive exercises. The paper will discuss the initial survey conducted that helped to serve as the basis for the development of the symposium, the symposium exercises, and the results of the survey regarding the efficacy of the symposium.

Although construction management (CM) programs in higher education are fairly young, graduates from over 100 CM programs in the United States enjoy nearly 100% placement rates and some of the highest salaries of students with bachelor’s degrees. However, CM programs are faced with several challenges, including: the negative image of the construction industry which can cause students to not consider a CM degree, the tension that exists in higher education between upper administration’s desire for research productivity and CM programs’ focus on undergraduate education, the difficulty in finding a faculty member that has both extensive industry experience and a Ph.D., a lack of research funding in CM-related areas, and declining state support for higher education to maintain a quality program. Although these challenges are significant, CM programs serve a unique need of the construction industry that is complimentary to civil engineering and architecture programs. The CM program at Colorado State University (CSU) has gone beyond the typical levels of industry engagement and instead has partnered with construction industry leaders to improve recruitment, student experiences, research needs, and quality of the program graduates. This case study could be a model for construction education programs in the future.

Women comprise only 9% of the entire U.S. construction industry workforce and even a lower percentage in many developing countries in Asia (7.5%) and South America (0.5%). Educational institutions can help resolve this problem by providing proper training to underrepresented minority (URM) students and encourage them to pursue a successful career. Since many students from developing countries who study abroad often choose not to return home upon graduation, those who do not leave their home countries in the first place become the real assets for the growth and development of their homelands. The first women in construction workshop (WCW) was organized and hosted in Orlando, Florida in partnership with two universities from China and Brazil to target this latter population. Through a rigorous application process, a number of female students were selected to attend the event which included discussion sessions and seminars led by several female construction faculty, career development experts, and industry practitioners. This paper presents the program details, outcomes, assessment results, and success stories to demonstrate the impact of training female graduate students as future role models on recruiting and retaining younger generations of women and minorities in developing countries.

Front end planning (FEP) is broadly viewed as an essential practice for project success. As a key deliverable of FEP, the project execution plan (PEP) is a fundamental document to guide project execution from design through construction and turnover to the client. However, the alignment aspects of PEPs are understudied. Construction Industry Institute (CII) research team (RT) 310 studied PEPs and found they lack provisions related to alignment. The research team identified 23 PEP elements central to alignment and surveyed 200 construction professionals to determine the criticality and frequency of the competent inclusion of these elements in capital projects. Summary results are that all 23 elements are important to alignment (one just slightly below an important score) and that the 23 elements are also not commonly included in PEPs in a competent manner. The survey included both owner and contractor organizations in the North American industrial construction sector. While there are small differences between owner and contractor responses, overall findings show common results that the industry strongly regards the 23 identified elements as important to project alignment. Similarly, there is common perception that the 23 elements are not included in PEPs in a competent manner on a consistent basis. Consequently, the PEP elements identified by the research provide an immediate set of action points for industry improvement.

The Construction Industry Institute (CII) has developed a suite of tools, namely the Project Definition Rating Index (PDRI) tools, to provide a structured front end planning approach for industrial, building, and infrastructure project types. CII has developed two tools for industrial projects: the PDRI—Industrial Projects, meant for use on large, complex projects, and the PDRI—Small Industrial Projects, for use on lower complexity projects. Previous research has shown that many aspect of small and large projects differ, but no direct comparison between the planning practices of these two distinct project types has been completed. This paper provides a detailed quantitative and qualitative analysis of the two industrial-focused PDRI tools discerning the specific front end planning similarities and differences between small and large industrial projects. Three conclusions were found through the analysis: (1) the rigor of the front end planning process should match the rigor of the project at hand, (2) small industrial projects have more pronounced renovation and revamp project considerations during front end planning than large industrial projects, and (3) the front end planning process for small industrial projects should be more focused on project execution as opposed to project feasibility.

The Construction Industry Institute’s (CII) Project Definition Rating Index (PDRI) effectively enables front-end project planning for large complex projects by providing a structured planning process for use during the front end planning phase of a project. The PDRI quantitatively scopes (scores) the level of project definition, and correlates the level of scope definition to typical project success factors so that project stakeholders can determine whether to move a project forward into design and construction. In 2015, CII initiated a research team tasked with developing a PDRI for small infrastructure projects to support front end planning efforts for this common project type. This paper presents the first step in that research effort, determining the definition of a small infrastructure project. The authors hosted focus groups and disseminated a targeted online survey to determine what constitutes a small infrastructure project. The authors found that practitioners separate small projects from large based on the complexity of the project; thus, this paper presents the primary factors and their associated breakpoints (i.e., total installed cost and engineering hours) and contributing factors (i.e., construction duration, core team numbers, and availability) that determine complexity on infrastructure projects.

Speed and quality of construction have always been a driving factor in the construction industry. In today’s era of technically complex projects, a variety of concepts and delivery methods have been defined all striving to satisfy the ever-increasing demand for faster project delivery from concept to completion. For reasons ranging from emergency rebuilds to conditions related to competitive market forces to regulatory compliance considerations, a higher level of fast tracking is needed offering greater predictability and even faster project deliveries. Hence, the pursuit of “faster fast track” or “flash track” deems necessary. This research defines flash track as a time-driven project, which by necessity requires a heightened degree of concurrency between engineering, procurement and construction. A principle research question was to identify and prioritize practices and strategies that could be employed to compress project durations, while maintaining safety, quality, and risk tolerance. This paper reports on the results of structured interviews, which were conducted on three successful flash track efforts, including an emergency rebuild project, a contractual integrated project delivery (IPD), and a major industrial project that embraced lean construction practices. Content analysis from these interviews yielded fourteen flash track practices, which were subsequently vetted by sixty-four fast track experts in a 3-round Delphi study to determine the degree to which these practices were essential for the successful delivery of flash track projects. These structured interviews provided a common basis in search of identifying leading and distinguishing flash track considerations.

Even though there are numerous planning tools and templates, projects still experience costly stops or holds. Moreover, since at least one stakeholder on virtually every project benefits from a premature start to construction, project teams nearly always feel pressure to begin construction—whether or not they are in fact ready. When a project starts construction prematurely, the result is frequently that it experiences an interruption to construction at least once. Complicating matters, when projects experience these stops and starts, project teams often seem to react rather than take a proactive management approach. Research in this area is dated and generally more focused on project readiness, effects of field rework, project change management, cost/schedule controls than on actual investigation of premature starts. New research is needed to better document premature starts and their impacts. The purpose of this paper is to describe the process used to identify and document leading indicators, drivers, and impacts of premature starts to construction using qualitative case study based research and quantitative survey based research. Common factors that can be seen on multiple projects of various scope definition, industry sector, and total project value were identified. Examples of drivers include time to market, seasonal and weather constraints, and contractor mobilization to start billing. Leading indicator factors that were also discovered include incomplete engineering documentation, unclear project objectives, and contract terms that incentivize early mobilization. Likelihood of these items occurring were quantified through survey based research.

The subjective and inconsistent use and interpretation of metrics related to progress measurement, performance assessment, and performance forecasting is a major obstacle to understanding the actual conditions of a construction project. In order to foster metric standardization for project controls, this study focuses on the construction phase to identify core metrics in progress measurement, performance assessment, and forecasting. First, an extensive list of metrics was extracted from a comprehensive literature review of both academic and industry sources and reviewed by a panel of experts to ensure that no important metrics were excluded. Second, an online survey was created and distributed to industry practitioners to capture project-specific information regarding metric usage and interpretation. Finally, metrics were evaluated in terms of their usage and importance; these results were compared to statistical analyses of key metrics closely linked to better cost and schedule performance in construction projects. Comparison revealed the gaps between perceived and quantitative relevance of metrics to overall project performance. Results show that out of 17 highly rated qualitative core metrics, only one, schedule variance, was among 11 quantitatively effective metrics.

Ensuring quality in a design deliverable is important and affects overall construction project performance. However, there has been insufficient study into which types of deliverables present the greatest quality challenges. To address this issue, the Construction Industry Institute initiated Research Team 320 to develop definitions and measurements of the quality of engineering/design deliverables. The primary focus of this paper is to prioritize engineering/design deliverables and to identify the most problematic deliverables. The researchers conducted a literature review, which identified 53 types of common engineering/design deliverables. The team then determined that 11 design deliverables are particularly problematic when it comes to quality. This was based on a survey of 67 industry professionals involved in design and/or quality management. Analyzing 53 deliverables, the team found both similarities and discrepancies in relative frequency of design quality problems between owners and contractors. The 11 most problematic deliverables include (1) P&IDs; (2) constructability input; (3) vendor data; (4) maintainability input; (5) level 3 baseline schedule; (6) equipment specifications; (7) piping routing and isometrics; (8) 3D models; (9) FEED validation; (10) nozzles/ladders/platforms for towers/vessels/tanks; and (11) miscellaneous pipe support drawings. Defects in these 11 problematic deliverables should be analyzed to substantially enhance overall project performance.

The process of selecting suppliers is a strategic process involving multiple departments within an organization. It directly impacts the performance of time, safety, quality, and cost indicators, and if properly performed can give a competitive edge to companies delivering engineering procure construct (EPC) projects. This paper presents the results of a research project sponsored by the Construction Industry Institute (CII) on the topic of supplier quality surveillance (SQS) and suppliers’ ability to provide products that conform to specifications. The paper specifically addresses the need to better understand the process of supplier selection and evaluation in the EPC industry using data gathered from multiple sources: literature, purchase orders (POs), procedures provided by CII member organizations, and face-to-face discussions with subject matter experts (SMEs). Supplier selection process maps were developed to illustrate how the process in the EPC industry unfolds and compares to what is found in the industry at large. Additionally, the study revealed that quality and delivery time are the most important characteristics of prior performance of suppliers, considered by EPC purchasers, followed by experience and cost. In general, items grouped under Procurement and Quality are the categories of prior performance that contain characteristics used most often to select suppliers.

The engineering and construction (E&C) industry is increasingly utilizing geographically distributed, virtual teams to execute project work, and virtual team technology continues to improve to support higher levels of virtual team performance. However, virtual team performance can vary widely across organizations. While virtual team technology is advancing, the technology alone is not sufficient and managerial aspects also need to adjust accordingly. To explore this topic, the Construction Industry Institute (CII) formed Research Team 326 to investigate how virtual teams can maximize performance. A survey questionnaire was distributed to individuals with experience working in virtual teams in the E&C industry. Respondents were asked to each evaluate factors for both a high performance virtual team (a team that substantially exceeded expectations of scope, quality, schedule, budget and client expectations) and a baseline performance virtual team. We then analyzed the team building behaviors that may be moderating virtual team performance by comparing responses from baseline and high performance virtual teams. We found significant differences between high and baseline performance virtual teams that may help E&C organizations to identify and improve the performance of their virtual teams.

The life cycle of the built environment consists of new construction, then rehabilitation and, finally, removal. This concept has taken on a unique importance with the widespread property abandonment in certain urban areas such as Detroit, Michigan. Large concentrations of abandoned properties cause blight and lead to social decline which threatens the public health and community welfare. With increases in the amount of structural abandonment comes issues of government intervention, funding of removal operations, and the large-volume of waste stream generated from removal of these structures. A new area of focus, known as “Domicology” is emerging in response to this abandonment crisis. It examines the life-cycle continuum of building and infrastructure abandonment, and studies the policies, practices, and consequences of human structural abandonment with the goal of finding approaches to reduce the negative environmental, social, and economic impacts of such unsustainable abandonment. The objectives of the research include an overview of various approaches and associated costs for demolition and deconstruction, and development of a comparison matrix for demolition and deconstruction. In addition, it discusses parameters related to urban blight removal in Detroit, Michigan. It is hoped that this discussion will help promote environmentally responsible options in the removal of urban abandoned structures.

Steel slag is the molten byproduct from steelmaking operations that is subsequently air-cooled for use. It has been used in cement manufacture and as unbound granular materials in construction. While numerous studies have revealed that concrete containing steel slag aggregate possesses good mechanical properties, and slag, as a byproduct, is cheaper than virgin aggregates, its practical use in Portland cement concrete is currently a forbidden area in the construction practices. What is the practicality, where is the crux of converting research results into to real production, and how to build the bridge between the laboratory experiment and the end products? Based on the philosophy of slag utilization, i.e., laboratory testing, field demonstration, and criteria establishment, the questions to be answered includes (1) two distinct laboratory test methods to determine the expansion force generated by slag particles; (2) conversion of the expansion force of mass slag aggregate to the expansion force of single slag particle; (3) mechanical disruption model of slag failure; and (4) usability criteria for the use of steel slag in a rigid or restrained matrices. The paper introduced the answers for the first two questions; and provides the mechanical model and criterion deduction to answer the other two questions that lead to criteria and specification establishment.

Today, the booming construction industry in Ethiopia is leading to an increased demolition of concrete structures whereby these demolished structures are disposed at landfills. The current practice is now creating a huge amount of construction and demolition waste over large landfills and is becoming the main source of shortage of land for infrastructure development. On the other hand, for the booming construction there is a huge demand of virgin aggregate for new concrete works. This paper discusses the potential use of demolished concrete from site-tested specimens as a recycled concrete aggregate (RCA) material for new concrete production. The study compares the cost of conventional concrete (CC) with recycled aggregate concrete (RAC). The output of this study will highly impact the growing construction industry and communities in Ethiopia thereby reducing waste, saving cost, conserving natural aggregates, building capacity, and setting quality standards.

The performance of new asphalt concrete overlays is often jeopardized by existing cracks in the pavement to be rehabilitated. These existing cracks propagate, or reflect, through the new asphalt concrete to the surface of the new overlay. Once reflection cracks appear on the surface of the new pavement, water and debris can enter the pavement structure affecting pavement strength and reducing the life of the overlay. Therefore, reducing the rate at which these reflection cracks propagate to the surface of the pavement is desirable. The research reported in this paper studied the effectiveness of two new reflection crack attenuation techniques. One technique is a grid reinforcing system, the other, a polymer modified asphalt-rich interlayer system. Each system included a control section where no reflective crack attenuation treatment was used. Performance of the two systems was evaluated for six years by measuring cracking semi-annually and comparing this to the cracking present before rehabilitation. After six years significant differences in performance between the controls and the attenuation systems have appeared. Results of this research provide new information regarding tools that may help extend the life of asphalt overlays placed on cracked substrate pavements.

Warm mix asphalt (WMA) describes various technologies that allow asphalt mixtures to be produced at lower temperatures as compared to hot mix asphalt (HMA). The objective of this study was first to evaluate the laboratory performance of plant-produced lab-compacted mixtures utilizing various WMA technologies and to compare WMA energy consumption cost and emission data to conventional HMA mixtures in terms of fuel/energy savings at the plant and in terms of CO and CO₂ emissions. Three projects in Louisiana utilizing four different WMA technologies were considered in this study. Laboratory tests included dynamic modulus (|E*|), flow number (FN), loaded wheel tracking (LWT) test, indirect tensile (IDT) test, semi-circular bend (SCB) test, thermal stress restrained specimen test (TSRST), and the Lottman moisture susceptibility test. Results indicated similar high and intermediate temperature performances. On average, $1.61 of energy savings per ton of produced asphalt mixture was observed, along with a considerable reduction in air pollutants at the plant. However, the cost of additives and royalty fees would reduce the total cost savings from using WMA.

Panelized walls system is a commonly utilized building prefabrication technique, where the building façade and partitions are fabricated offsite. In order to achieve prefabrication maximal benefits, commonality between the fabricated panels needs to be maximized by utilizing similar geometries and components. However, increasing the commonality between the panels would increase the deviations from the original panel design and increase material waste of oversized common components. The objective of this paper is to develop a platform design optimization model of customized cold-formed steel wall panels that optimizes the commonality between different fabricated panels to minimize overall production cost while minimizing the total deviation from the original architectural requirements. The proposed methodology is developed as a multi-objective optimization model that optimizes the commonalities between the wall panels and their elements in order to achieve two main objectives: (1) minimize the total production cost that includes labor and material cost; and (2) minimize the discrepancies between the produced panels geometry and the original architectural design. The model is able to generate a Pareto set of panelized wall designs that represent possible optimal tradeoffs between minimizing the overall production cost and deviations from design requirements.

In this study concrete mixtures with nanosilica particles (NS), silica fume (SF), and fly ash (FA) were tested for compressive strength, permeability via the rapid chloride ion permeability test (RCPT) and the costs were estimated for each mixture design. Model equations were generated from the empirical data and used to predict compressive strength and permeability for mixes containing NS, SF, and FA, with dosages within the tested range. After establishing the accuracy of the predictive model equations, three goals were established. The first objective was finding the optimal dosage of NS when it is the only admixture considered; the results indicate that the highest level tested of NS%=3 is the optimal dosage. The second objective was to find the maximum compressive strength and lowest permeability when all three admixtures are considered, for both tests, the best performing mixture contained NS%=3, SF%=20 and FA%=0. Finally, the predictive models were used simultaneously in order to find mixtures with the best cost. The predictive models found mixture designs that were able to improve performance, while simultaneously reducing costs when compared with the control mixture.

Lime is a traditional construction material, mainly composed of calcium oxide (CaO), calcium hydroxide Ca(OH)₂, and magnesium oxide (MgO). It is derived from the calcination of limestone and has been used for construction since ancient civilizations. Through the years, the construction industry has sought to improve the chemical and physical properties of lime to boost the durability and mechanical behavior of the material, and consequently increasing the service life of buildings. However, its use has been declining, and favoring of the use of Portland cement mortars. The latter shows adequate properties, but its manufacture releases high doses of greenhouse gases into the atmosphere. This research focuses on the use of mineral admixtures to improve durability of aerial lime paste with the intention of increasing its use in masonry works. In addition, it proposes an alternative material to reduce some of the use of Portland cement mortar in construction sites. In this research, the research team tested different admixtures with slaked lime mixture to observe the behavior of the new material. A total of eight mixtures were prepared. A constant volume of aerial lime paste was used with the following admixtures: river sand, fly ash type F, brick powder, and nylon fiber. Two types of tests were performed. The first was the capillary absorption test using an adapted version of ASTM C1585-13, and the second test was performed to verify the compressive strength using ASTM C109. The results show significant improvement of the mechanical properties and durability of aerial lime paste with mineral admixtures. These combinations provide an environmentally friendly construction material that can be used in masonry works. This paper contributes to the body of knowledge by increasing the understanding on the use of mineral admixtures in aerial lime as an ecofriendly alternative to Portland cement mortars.

Considerable efforts are currently being undertaken worldwide to utilize rice husk as an alternative cementitious material. Although the use of Rice Husk Ash (RHA) has been practiced since the 1980s, an investigation of this supplementary cementitious material has not been assessed in the developing world. In this study, a detailed experimental analysis is conducted to determine the compressive strength and workability of concrete by partially replacing ordinary portland cement with RHA as part of a case study in the Ethiopian construction industry. Two control mixes, and cubes with varying RHA proportions of 5, 10, 15, and 20% and including admixture are tested to attain a compressive strength of 25 MPa. The cubes are cured in a laboratory setting and compressive strength measures of 3, 7, 28, and 56 days are recorded. The results indicate that 5 to 15% of RHA replacement shows a better compressive strength than the control mixes at 28 and 56 days but the workability decreases with an increase in RHA replacement which aligns with prior studies. This study will allow the development of a mix ratio and considerations in design standards not only to the Ethiopian construction industry but also other developing countries in Sub-Saharan Africa.

This paper proposes a model for a financial assessment of construction companies. This model is derived from the combination of data envelopment analysis (DEA), entropy method (EM), and TOPSIS. In the first step of the model, the financial ratios are calculated based on balance sheets and income statements. In the next step, the 15 ratios are categorized and classified utilizing a combination of TOPSIS and EM methods. Finally, they are aggregated in six indicators. Then, the BCC model of DEA with the type of output-oriented is used for these six indicators to calculate companies’ relative efficiency. In the paper’s conclusion, the model is examined in 20 construction companies that obtained analysis could have a high value for comparing the companies.

A public-private-partnership (PPP) is a contractual arrangement formed between public and private sector partners, which allows for more private sector participation than traditionally possible. Emphasis is placed on expanding the private sector’s role in designing, constructing, operating, maintaining, and especially financing infrastructure projects. Design-build-finance (DBF) is one prominent type of PPP. The goal of this research is to quantify the various funding sources of DBF PPP projects in the U.S. transportation sector. The methodology consists of a review of the existing PPP literature on DBF practices in the U.S. and abroad, followed by data collection through a combination of public and private sources, published data, and a series of structured interviews with project stakeholders. The paper presents preliminary results stemming from 21 ongoing and fully completed DBF transportation projects, showing private contributions (of debt and equity) account for more than a quarter of the DBF funding for this subset of projects. Overall, this study contributes to the body of knowledge by evaluating the various funding sources of DBF projects and quantifying the potential of private financing sources to accelerate the amount of infrastructure delivered to the public.

The World Bank indicated that inadequate infrastructure hinders local economic growth and competitiveness and it represents a major cause of quality of life loss. This research studied the proposal for a new multipurpose building at the University of Puerto Rico at Mayagüez (UPRM) through a public-private partnership (PPP). The proposed building seeks to address several problems, including lack of parking spaces and flooding problems in the current parking site. The project proposed consists of a multi-story parking with approximately 960 new parking spaces. In addition, is proposed to build an underground pond to manage the storm-water and a new commercial space of 50,000 ft². Nevertheless, the decision-making is difficult and can be controversial because private initiatives are traditionally rejected by the community. sThe main objective of this paper is to study the delivery method options for the procurement of this infrastructure. Several options of PPPs were evaluated for the essential basic services. The decisions on whether a project is implemented as a PPP should depend on whether “value for money” (VFM) will be achieved. During the first phase of this research UPRM students had the opportunity to express preferences and concerns about possible rates for the use of the new parking. In order to identify current demand of parking space and campus necessities, 1,000 students were surveyed. The second phase of this study included the implementation of VFM calculation for the proposed multi-story parking building. A cost analysis with industry prices of design, build, operation, maintenance, and financial cost was also included on this study. Preliminary results showed that the students are willing to pay a fare for the use of this infrastructure, even though the existing parking space can be used for free. The construction budget was estimated to be close to $26M with a pay back of approximately 21 years. This paper contributes to the body of knowledge by including community participation in procurement of infrastructure.

A proper understanding of the local construction market is essential for making appropriate project budgeting and planning decisions. State highway agencies typically use highway construction cost indexes (HCCIs) to understand the current market conditions. In the U.S. highway construction industry, the Federal Highway Administration (FHWA) pioneered the concept of a HCCI as an indicator of the national construction market. State Departments of Transportation (DOT) also started developing their state level HCCIs to better represent their state level construction markets. But, some state DOTs noted the lack of guidance to develop and update their HCCIs. This paper summarizes literature review and nationwide questionnaire survey results to identify the current practices of calculating and using HCCIs. There are two methods to generate basket of construction items for HCCI calculation: a) categorized market basket and b) item level market basket. The Fisher index is the most popular indexing formula among the state DOTs and is also recommended by the FHWA and International Monetary Fund (IMF). Despite many potential users of HCCIs, the current use of HCCIs is very limited in state DOTs.

Three of New York’s public transportation agencies have embarked on the construction of design-build Mega projects. The NYSDOT Kosciuszko Bridge, The NYSTA New NY (Tappan Zee) Bridge and the PANYNJ Goethals Bridge are all cable stayed bridge structures utilizing the design-build project delivery method. Although these mega bridge projects are currently in various phases of construction, the procurement methodologies have been reviewed, analyzed, compared, and contrasted using a review of the RFP documents, selection criteria, and interview data gathered from key project pursuit team and RFP development team members. The initial findings are discussed in detail and will be utilized as they key areas of future research in developing a RFP scoring criteria development model to maximize the benefits of utilizing design-build project delivery and lead to further research in the procurement practices of mega-bridge construction projects.

Small margins coupled with heavy competition for projects, mean any chance to augment profits or to gain an upper hand in bidding should be investigated by general contractors. Cost segregation studies represent one such chance. A detailed estimate should have the data necessary to produce a cost segregation study potentially positioning a general contractor to provide this service. To explore the ability of a general contractor providing cost segregation reports, this study evaluated a cost segregation report produced by a general contractor’s employee using the detailed estimate. The accuracy of the report and benefits available to both owners and general contractors were considered. The results suggest that on the right types of projects, general contractors are able to accurately provide this service.

The use of alternative project delivery methods, such as design-build (DB) and construction manager-at-risk (CMAR) in the design and construction of water and wastewater projects has been hindered by several impediments existing for owner organizations. This study identified these impediments as well as possible solutions when using these delivery methods. A questionnaire was designed and sent to utility managers and wastewater owners of the Water Environment Federation and the American Water Works Association, and 104 responses were received. The results showed that the majority of respondents—representing 32 states in North America—faced such internal impediments as “unfamiliarity with the process” and “resistance to change within management or policy officials” when these delivery methods were used. In addition, most respondents faced external impediments, such as “state procurement statutory requirements” and “municipal or agency procurement regulations.” Recommendations based on this study include educating and training the decision makers (policy officials, project staff, and procurement and legal staff) regarding these delivery methods and changing the state procurement rules.

The U.S. construction industry is characterized as adversarial; however the same can be said for the construction industry in countries that have adopted alliance contracting as the project delivery method of choice for megaprojects. This paper builds on the results of a rigorous case study analysis of nine alliance megaprojects from Australia, the Netherlands, New Zealand, and the United Kingdom. The research found that alliance contracts were best suited for extremely complex, high-risk projects where a large number of external stakeholders demand a highly integrated and highly collaborative project delivery team. This paper extrapolates the alliance project characteristics to the U.S. infrastructure market by comparing the international projects to a U.S. case study project that the Washington State Department of Transportation prevented from delivering as an alliance contract by uncertainties in the legislative authority about key concepts in alliancing such as the “no sue” clause. The paper goes on to develop a framework for making a business case for implementing alliance project delivery for complex infrastructure renewal projects by a typical U.S. state public transportation agency.

Numerous studies have examined the impact of project delivery strategies on project objectives such as cost, schedule, quality, and satisfaction. Additional literature has applied the principles of transaction cost economics to investigate organizational structures in the construction industry. However, little research has examined the impact of project delivery strategies on the owner’s transaction costs, those internal costs of developing, procuring, and administering construction contracts. This paper discusses the challenges of capturing owner transaction costs and examines the potential for simulation modeling to estimate such costs. The authors describe and evaluate the three major simulation paradigms—discrete event simulation, system dynamics, and agent-based modeling—in the context of construction contracting transaction costs. Simulation modeling offers the ability to quantify uncertainty and examine the sensitivity of numerous variables of existing contract processes. Methods of organizational analysis for this study include a high-level, top-down analysis that supports a system dynamics approach, and; a step-by-step, bottom up process analysis that supports discrete event simulation. As a part of a larger research effort, these results will be incorporated into a decision support tool for public owners to select appropriate project delivery strategies to optimize allocation of both human and financial resources.

BIM-based cost estimating methods can potentially improve the efficiency and effectiveness of the conceptual design process. However, research is still needed to understand how to apply these systems in practice, and to quantify the effect of doing so. This paper uses a retrospective case study for describing a conceptual design process in which the design team developed the shading strategy for a university building. A detailed process map describes the information the design team constructed and shared during the development of the process. The research then contributes a speculative future state process map that applies modern BIM-based cost estimating methods to projects, and defines process metrics to describe their efficiency and effectiveness. A comparison of current and future state process maps and metrics enables a discussion about the potential costs and benefits of BIM-based estimation methods, and helps identify areas for future research and application.

On a construction project, “work structuring” refers to the design of the production organization, and includes the division of the work into work packages and the assignment of the work packages to the different project participants (trade contractors). Work structuring determines the scope of each work package, and the dependencies between work packages. Consequently, it determines the need for coordination between the project participants. This study investigates the criteria that project managers use to decide the breakdown of the project into work packages. Data were collected on three projects through interviews with project managers, and review of project records. The findings identified two sets of considerations influencing the work structuring: (1) Considerations related to winning the project, including the owner’s requirements and budget constraints, requirements for use of local/disadvantaged/minority contractors, prequalification requirements, and licensing. (2) Considerations related to production performance, including the difficulty and complexity of the work packages, the quality of the subcontractors who will be attracted, interfaces and coordination requirements, and potential production problems. These factors often create conflicting requirements and trade-offs that can affect project performance.

As the United States public-private partnership (P3) market continues to mature, the rationale for increased P3 activity is increasingly under scrutiny by government officials, non-governmental organizations, taxpayers, among others. One key argument is that P3s generates innovation, which contributes towards greater value for money (VfM), when compared to conventional delivery methods. Yet, empirical evidence of innovation is scarce. Several research studies, however, have examined this issue. Thus, a comprehensive appraisal of this archival literature is done to uncover each article’s: (1) theoretical grounding, (2) context and scope, (3) methodological basis, (4) approach for defining and assessing innovation, and (5) findings and conclusions. Consequently, this investigation synthesizes this existing archival research on innovation in P3s to characterize and compare the work that has considered this issue. In particular, the results evaluate the evidence to date regarding innovation in P3s, whether it is occurring or not, and what are its barriers or enablers. Findings suggest that past studies have found limited evidence of P3 innovation, but incremental improvements in value are present.

This paper develops a methodology to calculate the appropriate discount rate for road concession projects under the public private partnership (PPP) model in Colombia. This methodology is applied to the government’s fourth generation concession program (4G) as a case study. The paper includes an analysis and description of the infrastructure sector in Colombia and explains an ambitious program for road development called 4G, which involves investments for more than US$20 billion in the next decade. Then, the discount rate is estimated using different scenarios and an expected value of the variable is constructed from a range with a confidence level of 95%. A theoretical and empirical analysis is performed in order to validate the appropriate discount rate according to industry standards. This value will be supported by different analysis and various experiments conducted throughout the paper. Finally, it is possible to compare this discount rate with the yield the government is currently offering to the investors, and the result will suggest that the government is underestimating the risks associated to the projects.

The Queen Elizabeth II Highway provides the sole transportation link between the city of Edmonton and the city of Leduc. Over 35,000 vehicles commute between these areas on a daily basis, while the Edmonton International Airport is strategically positioned between them. The Canadian government has recognized the need for an alternative transportation corridor in this area. The necessary funding for Phase 1A of the 14.4 mi of new road construction was provided under the Building Canada Fund. The proposed Nisku Spine Road will be completed in four separate phases. The objective of this study was to draw analysis from a government’s implementation of a value-based procurement model and risk management approach to minimize project cost. Under this procurement model, the overall project costs were reduced through past contractor experience, risk assessment plans, and value added options. The selected firm’s proposal was 30% below the original anticipated cost of $21.8 million dollars. Furthermore, the contractor was able to offer a value added option that resulted in additional savings of approximately $1 million dollars. In all, a total savings of $7.5 million dollars was realized by the utilization of the value-based procurement method for this publicly funded heavy/civil construction project.

The public sector vertical construction market has historically procured contractors through price-driven methods. Yet the increased use of alternate project delivery methods along with owner-perceived concerns over construction performance has led many owners introduce qualifications-based selection criteria into their procurement systems. As a result, construction contractors are being tasked with preparation of technical proposals ever more frequently when pursuing public sector work. The research objective of this paper was to identify whether quantity and quality content elements of contractor technical proposal have a relationship with the owner evaluation committee’s proposal evaluation scores. The methodological approach was to perform a content analysis on a sample of 50 contractor proposals (containing selected and unselected bids). Results indicate that certain quantity and quality elements do in fact have direct relationships with owner evaluation scores, and to varying degrees. The quantity of contractor-identified project risk solutions as well as proposal content that emphasized solutions to potential design errors and omissions, concealed existing conditions, and technical elements of the contractor’s work plan, corresponded with higher evaluation scores. The quantity of proposed scope alternates also correlated with higher evaluation scores, along with scope alternates that were expected to have negligible impacts to project cost.

In order to perform road maintenance activities in the United States, the state Departments of Transportation (DOTs) either use in-house resources or outsource the work to private contractors. The literature indicates various reasons for outsourcing the projects to private contractors, and cost saving is one of them. This study collected maintenance cost data of culvert cleaning and sweeping projects performed by in-house resources and private contractors in Nevada. Their unit costs were compared to see whether Nevada DOT reduced cost by outsourcing. In addition, a customer satisfaction survey was conducted to see whether road users were more satisfied with sweeping of roads when the work was performed by in-house staff or when it was outsourced. Results of the data analysis showed that culvert cleaning and sweeping performed by in-house resources were much less expensive when it was outsourced. Based on the survey results, customer satisfaction regarding sweeping showed that, on average, local road users were significantly more satisfied with the work performed in-house than when out-sourced.

Construction management processes are focused on technical aspects with minimal consideration for social factors. Among these is trust, which has a prominent impact on project efficiency and success. According to a Construction Industry Institute study, among the factors impacted by low trust are efficiency and administrative costs. Trust is defined as “a hard edged economic driver” that affects speed and cost—two of the three pillars of project management. Preliminary data in the literature, provides evidence that trust is ignored in construction management. To address this gap, this paper identifies trust’s impact on construction bidding processes as perceived by professionals. The adopted research methodology utilizes a survey instrument to identify important parameters upon which selection processes depend, and characterize industry professionals’ view about trust’s effects on the process. The results indicate that trust, even if not explicitly stated, plays a major role in such processes. Furthermore, there is a direct correlation between trust level and contractor/subcontractor selection among all project delivery methods. The results emphasize the impact of trust on bidding strategies and provide decision makers with better understanding of their actions.

Changes in construction projects increase final costs and reduce productivity. Change of scope, unforeseen existing condition, and design errors/omissions are considered as the major causes of changes in construction. For over half a century, scholars have evaluated the origins and causes of variations, while others classified the changes to be able to predict each type’s occurrence. The parties’ level of information plays an important role in change formation. The contract settings may persuade the parties to keep the information private to benefit from the changes. Information asymmetry, which happens when one party has more information and ability to predict changes earlier, can result in contractual hazards. Since the contractors have usually been blamed for concealing information; this study investigates this allegation by analyzing unit price contracts from California Department of Transportation (Caltrans). It focuses on items that have experienced a change in quantity and statistically analyzes if contractors have overpriced these items in the bidding process. The outcomes of this study show that contractors seem to be able to predict items with underestimated quantities. This finding persuades the owners either to incentivize the contractor to share information or change the delivery method, thus, reduce the probability of change order occurrence.

Disputes are common in the construction industry and lead to unnecessary cost and schedule overruns in projects. It is commonly believed that as the level of trust increases among project stakeholders, the frequency and severity of disputes decrease in a project; however, no previous study has empirically tested this hypothesis, especially in highway public projects. Therefore, this study empirically investigated the impact of various categories and levels of trust on the frequency and severity of disputes in public highway projects. Comprehensive literature review was conducted to identify factors that impact disputes in a construction project; this review yielded a survey instrument that captures both independent factors—including three categories of trust (competency, organizational, and relational)—and dependent factors—including the frequency and severity of disputes as well as other project performance metrics related to disputes. Then, the survey was distributed to procurement personnel in state Departments of Transportation (DOT). The data obtained was analyzed to find any statistically significant difference between dispute frequency and severity, relevant performance metrics, and the various categories and levels of trust. The results consistently revealed that high trust across categories resulted in high-performance projects for the aspects studied and organizational trust had the highest significant positive impact on the project performance metrics studied. An interesting observation was that, in most cases, neutral trust—not low trust—corresponded to the lowest levels of project performance for the aspects studied and across all categories of trust. The findings of this study can help DOTs appreciate and facilitate trusting environments for their projects to decrease the frequency and severity of disputes in the construction process.

Partnering is a construction contracting technique formally developed in the 1980s and successfully used by the U.S. Army Corp of Engineers (USACE). Further, the Construction Industry Institute (CII) recognizes partnering as a best practice for creating an effective and efficient project team. Collaborative arrangements for construction projects are topics of developing efforts due to the competitive and opportunistic atmosphere inherent in traditional construction contracting that can result in unsuccessful project outcomes. Partnering provides for project teams to improve project performance with enhanced collaboration and cooperation among the key project participants. Yet, the use of partnering and the relationship that partnering has with the integration of a project team has not been studied effectively. This study aims to measure project team integration and partnering across a variety of U.S. construction projects. Of the projects studied, each experienced a level of effectiveness of partnering and the level of project team integration achieved. Once collected, the data was tested using association and correlation statistical techniques to find if partnering does influence the relationship of the project team and if its use can improve project outcomes. The results of the study reveal effective partnering can positively influence the integration of the project team.

Generally, western legal systems are “individualistic” and focus on where the individual rights and responsibilities of people or parties begin and end. These types of legal systems have historically been able to handle traditional construction contracts effectively. In addition, the identifiable and quantifiable risks in such systems have enabled insurance industries to develop and offer a range of products for construction contracts. In contrast, building information modeling (BIM) is “collaborative” in nature and is most effectively used when participants jointly create such models. Hence, contracts involving BIM are more challenging to put together. This challenge manifests itself in two main groups within the construction industry, which have different perspectives; architects/designers on one side, and the general contractors and subcontractors on the other. This paper investigates the current legal and contractual challenges of BIM which designers face today. The survey results reveal that the design-bid-build contract delivery method is still used in majority of these projects and BIM is not included in the contract documents on the majority of the projects. BIM has been included in such contracts, but only as a “co-contract,” “inferential,” or “accommodation” document and majority of the participants have confusion about the component parts, which constitute the record on the contract. This is perhaps because, under existing contract laws, including BIM would upset the traditional allocation of risk among the industry participants.

In 2013, the Danish government implemented a declaration for the use of information and communication technology in governmental buildings (ICT-declaration). This has led to an increasing number of building projects using building information modeling (BIM) with potentially better productivity and quality. In spite of documented advantages from using BIM, this case study shows that the possibility of delivering added value using BIM is a challenge because of several legal aspects. The existing laws are individualistic, and the BIM line of thinking is collectivistic. Therefore, BIM gives some matter of dispute in correlation to existing contractual terms i.e. displacement of traditional areas of responsibility, risks, legitimate trust to the building models and obscurity about ownership to digital information. This case study identifies several potential barriers for using BIM in the Danish Building Sector in the existing legislation as well as in the existing contractual terms. There is legal uncertainty connected to the use of BIM. There is no legal practice in the BIM area that refrains the contractors from achieving a maximum of advantage using BIM. BIM gives rise to interpreting the legislation in Denmark in a new and unknown context regarding responsibility, liability, and privileges between the parties in a building project.

The successful implementation of public-private partnerships (PPPs) requires the contractual obligations and rights of the public and private parties to be clearly defined. The occurrence of risks in PPPs may negatively impact the parties’ abilities to perform their obligations. Force majeure (FM) risks represent a risk category that requires delicate management as it may cause tremendous losses to the private party. However, little has been published on how these risks were defined, allocated, and managed in PPPs. This paper investigates these issues through a case study approach that analyzes the agreements of five PPP transportation projects in British Columbia through content analysis. The findings show that not all FM events are dealt with as FM risks; only the most severe are called eligible FM (EFM) risks. These risks provide compensation events that relieve the parties from their obligations, allow termination of contracts, and provide for compensating the balance of the private debt, equity, and labor payments. Non-EFM events provide for continuation and compensation for the expenditure above the maximum insurance coverage. If the expenditure and restoration time exceed particular thresholds, termination may occur. The analysis should assist the PPPs on how to better manage the FM risks.

Involvement of the private sector in financing highway projects in the form of public-private partnerships (P3s) is subject to various limitations and challenges that affect P3 project planning and development, and limit the expansion of the U.S. highway P3 market. Public and private sector stakeholders need to have a better understanding of opportunities for improvement that can contribute to the U.S. highway P3 market growth and promote excellence in P3 project planning and development. The main objective of this study is to identify and explore opportunities that can help state DOTs facilitate transportation planning and procurement of highway P3s. Following interviews with twenty P3 industry experts, we categorize the enabling solutions and recommendations for development of highway P3s into five areas. The recommended enabling mechanisms are then analyzed in detail. Results indicate that state DOTs can establish mature and transparent P3 programs in order to attract interested investors and promote the partnership culture between the public and private stakeholders. In addition, flexibilities in project procurement and consideration of operations and maintenance (O&M) services as a separate contract can incentivize private sector involvement. Finally, requiring a quality management plan as part of P3 proposals can alleviate quality concerns on P3 projects.

Risk is often discussed in connection with public-private partnerships (PPPs). Despite being central to the delineation and allocation of risks, contracts have not been used directly to examine risks in PPPs, especially in the US. This pilot investigation examines risk allocation and related provisions in four Virginia highway PPP projects whose total value is $5.4 billion. A developed risk matrix and rubric supported content analysis of the contracts, which identified 27 risks, their allocation, and their risk sharing mechanisms. The study consequently characterizes contractual risk allocation and provisioning in Virginia’s highway PPP market. Variances from project to project are typically related to project-specific risks, although some shifts were observed in macro-level risks. Future research will examine the rationale for unexpected allocation practices. The study will also be expanded to the US highway PPP market to explore contractual risk allocation and provisioning practices and to conduct comparative analyses over a range of parameters such as scope, jurisdiction and time.

Much of the water and wastewater lines in the United States are nearing the end of their useful life. A significant reinvestment is needed in the upcoming decades to replace or rehabilitate the water and wastewater infrastructure in the U.S. Currently, the traditional method for delivering water and wastewater pipeline engineering and construction projects is design-bid-build (DBB). The traditional DBB delivery system is a sequential low-integration process and can lead to inefficiencies and adverse relationships between stakeholders. Alternative project delivery methods (APDM) such as construction manager at risk (CMAR) have been introduced to increase stakeholder integration and ultimately enhance project performance. Schedule performance can have significant impacts on pipeline projects and can be a major contributor to cost growth. However, the schedule performance of CMAR projects in the pipeline engineering and construction industry has not been quantitatively studied. The objective of this paper is to develop a baseline for the schedule performance of CMAR water and wastewater pipeline projects. The research objective will be accomplished by administering a quantitative survey developed to facilitate the data collection from 53 projects, followed by a statistical analysis of the schedule performance data. The results show an average schedule growth of 1.77%, which is notably less than the typical schedule growth observed with other delivery systems. This research contributes to the body of knowledge by providing the first quantitative study of CMAR schedule performance for pipelines engineering and construction projects.

Change orders have a negative impact in construction projects because they can produce significant increases in cost and extensions in contract duration. This research identifies and evaluates the change orders causes in design-build-maintain projects under the school renovation program of the XXI Century in Puerto Rico. The Pareto diagram was used to identify and evaluate the major causes that contribute to increase costs and duration of the projects. There were 197 change orders analyzed in 40 schools. These resulted in an increase of 13% of the total contract costs, and an increase in project duration of 30% of the total contractual time. The most significant and important cause identified was owner directed changes. It was found that the owner (Authority for the Financing of the Infrastructure), and the user (Department of Education), should improve the communication and coordination, starting from the planning phase of the project. Therefore, one of the identified methods to reduce the changes that cause higher costs and time extensions is to establish better planning by the owner and user. Having planned well, the increases in costs and time extensions of the contract, due to change orders, would experience a reduction of 95% and 94%, respectively.

Technology is rapidly changing the way in which construction projects are delivered; the use of unmanned aerial vehicles (UAVs) is one such example of this. UAVs have many potential applications in construction from site layout, observation and inspection to monitoring safe practices and more. Unfortunately in the United States, the Federal Aviation Administration (FAA) regulations and state and local regulations often prevent or limit the widespread use of UAVs in many jurisdictions. In February 2015, the FAA released proposed rules for the use of UAVs. Among other things, those proposed rules limit the use of drones to daylight hours and limit the range of drones to the visual line-of-sight of the pilot. In May 2015, the FAA announced it would allow limited testing of UAVs outside the line of sight of pilots. Many states have also enacted or are in the process of enacting legislation to address the use of UAVs. This paper will examine the current and proposed FAA regulations governing the use of UAVs and their applicability to the construction industry, as well as highlight several state level legislative enactments. Additionally, the paper will discuss potential liabilities associated with the use of UAVs on construction sites.

There are two different directions considered for a subcontractor partnering in construction projects, horizontal and vertical. In horizontal partnership (partnering with other subcontractors) subcontractors can benefit considerably from joint resource management. While potentially all parties can benefit from partnership, enforcing it may be challenging in practice due to several reasons including lack of trust. Trust-based relationship is a divarication of collaborative behavior defined in this paper. By joint resource management, the overall payoff of coalition and the payoff of every partner increases, making the partnership more beneficial. In this article, a model is defined to build and analyze partnering between subcontractors based on game theory. Results show that this model helps to improve the win-win outcomes of subcontractors. The model contributes to the body of knowledge by quantifying the win-win outcomes of a horizontal partnership. It can be also utilized in practice to give subcontractors adequate information on the way they can behave, to improve their own payoffs while improving the overall payoff for the project.

During the estimation phase of megaprojects, many traits must be addressed and taken into account in order to obtain realistic results. Objectives, influential factors, and their interdependencies must be accurately identified and measured. As part of this process, it is crucial to determine the uncertainty around project objectives as well as risk events that may impact the project on various levels. This paper proposes a method of defining and incorporating the uncertainty and risk events around the two main objectives of any megaproject (i.e., cost and schedule). To serve this purpose, a stochastic event simulation model which operates based on Monte Carlo simulation and uses Microsoft Project and @Risk for Microsoft Excel as an integrated simulation platform has been developed. The goal of the proposed model is to incorporate uncertainty and risk into the project schedule and assess the variations of the model output with respect to the deterministic estimation of cost and schedule. The focus of this study is on megaprojects that incorporate both sequential construction activities as well as cyclical manufacturing activities. The project used to validate this approach is a nuclear plant Retube and Feeder Replacement (RFR) project in Ontario, Canada.

The existence of material price volatility in construction projects puts forward substantial risks for all parties involved. In particular, long-term construction projects. Depending on the parties involved in the project, types of contracts and state of the market, various strategies are practiced by contracting parties to manage project risks related to price volatility (e.g., price adjustment clauses, alternative project delivery methods, price cap contracts, contingency, early material procurement, quantitative methods of risk management, and etc.). Unfortunately, in many cases companies fail to select an adequate approach to better manage volatilities of material prices. The aim of this study is to identify critical project factors and align them to documented strategies to manage price volatility, based on an extensive literature review and industry interviews. Furthermore, analytic hierarchy process (AHP) is used as a selection tool to prioritize the top identified strategies with respect to various criteria (e.g., risk sharing, cost, schedule, scope, and type of the project). This study provides a selection tool along with practical guidelines to help various parties to make consistent, logical decisions for mitigating the risk of material price volatility.

The majority of pavements in the United States are made of asphalt materials. Many of these pavements are getting old and they require some form of rehabilitation. In many cases, recycled asphalt pavement (RAP) and recycled asphalt shingles (RAS) can be utilized to create a pavement system while saving money by conserving natural resources. There are four major asphalt production cost categories including materials, plant production, trucking, and field construction. In general, the materials are the most expensive category, in many cases up to about 70% of the cost to produce HMA mixtures. Several thousands of records of various field projects from 2008 to 2013 were obtained from South Carolina Department of Transportation (SC DOT) and all of the mixtures containing RAP or RAS were recorded and analyzed. The main objective of this study was to compare the upper quantiles of the unit cost between two groups of asphalt pavement mixes containing different percentages of aged binder using a Harrell-Davis estimator of the quantiles. In most cases, the asphalt mixes containing more than 15% aged binder actually appear to have a negative effect on cost predictability at higher quantiles.

Linear or repetitive projects (LRPs) constitute a significant portion of the construction industry. Although existing LRPs’ scheduling methods outperform network-based scheduling techniques, they suffer from limiting assumptions. For instance, line-of-balance leads production lines to maintain the same rate in each unit, even though it is more reasonable to increase and decrease production rates among the project units by hiring outsourcing crews or by excluding the initial assigned crews. This paper addresses this issue by aiming to improve existing LOB-based models and presents a total cost optimization model which accounts for changes in production rates, in addition to interruptions and construction methods. This paper treats the aforementioned problem using a max-min ant system (MMAS) algorithm. A repetitive project case example is presented to verify and validate the performance of the proposed model in terms of efficiency, stability, and accuracy. Compared to existing models, the proposed model—which considers variance in production rates—provides a positive influence on the optimal solutions as well as more flexibility for planers.

Various overflow structures are used to mitigate failure potential for dam overtopping. Arced labyrinth spillways are spillways folded in a specific pattern and arced to increase flow discharge efficiency. A geometric design optimization scheme for arced labyrinth weirs was employed to analyze the feasibility of the geometric design and provide cost effective infrastructure. The optimization scheme for arced labyrinth weir geometric design is proposed for use in a spillway performance-cost analysis. The spillway optimization method that was developed integrates hydraulic performance and cost estimation to find the most cost effective, hydraulically adequate alternatives for a particular set of site constraints. This paper analyzes a case study that utilizes the proposed optimization procedure and compares expected hydraulic conditions and specific site considerations for over 10,000 arced labyrinth weir geometric designs for a specific case. After applying the maximum design flood as a constraint, the construction cost of the spillway was calculated. The resulting analysis determined the 10 lowest cost arced labyrinth spillway designs that can sustain the expected hydraulic conditions. These final designs were then subjected to an economic and performance comparison to a full-scale linear labyrinth spillway to determine the feasibility of the designs. The arced labyrinth weir resulted in the highest value based on hydraulic performance and cost criteria when compared to a linear labyrinth weir.

While network-based scheduling techniques are not efficient for linear or repetitive projects (LRPs), existing LRPs’ scheduling techniques (such as line of balance; LOB) do not enjoy widespread popularity due to some probable flaws. Although LOB method aims to determine crew distribution to meet pre-specified deadline, it often fails to satisfy the deadline constraints. This paper aims to improve LOB method and present a novel heuristic line-of-balance (HLOB) algorithm for determining the optimal number of crews to accelerate resource-constrained projects with non-serial activities while satisfying the deadline constraints. The paper treats the problem as a mixed integer nonlinear programming (MINLP) and solves it with the proposed HLOB algorithm. To compare the results, the same problem is solved employing existing LOB-based models and the branch and bond algorithm. Compared to the existing methods, the proposed HLOB algorithm provides optimal/near optimal solution in significantly smaller computer run time. Due to less mathematical complexity, the practitioners may efficiently use the algorithm for scheduling of real-world projects and LRPs scheduling problems.

Major construction projects are often constructed by dividing the project into smaller, more manageable segments. Managing these projects is a complex task due to risk and uncertainty. Many studies have proposed risk analysis frameworks to analyze the impact of risk on project outcomes. However, a limited study has addressed the effect of considering correlations on the cost-risk analysis frameworks. Capturing correlation between variables during the risk analysis process is an important, but a challenging task. This paper examines the impact of correlation between project phases/segments on total project cost. The Federal Highway Administrative cost-risk analysis framework was investigated. The correlation coefficient assessment was determined based on a comprehensive review of literature and several risk management frameworks used in transportation and transit sectors. The paper includes a case example that discusses the impact of correlation on the total project cost uncertainty in detail. The findings from this paper contribute to a construction risk analysis and management domain by showing the effect of considering correlation on the cost-risk analysis process. The results from this paper also help the highway industry to better manage their projects by performing a more accurate risk cost estimate.

The current deteriorating state of the highway system in the Unites States is shifting the balance of highway construction projects towards maintenance and rehabilitation works on the expense of new construction. Hence, state highway agencies (SHAs) are now challenged to complete a substantial scope of highway rehabilitation work while controlling and reducing the negative impacts on the travelling public. Therefore, alternative contracting methods that are aimed at reducing highway projects’ durations, such as the incentive/disincentive (I/D) method, are gaining popularity among SHAs. For instance, the value of the ID assigned in a highway project has a significant effect on expediting the completion of the project while saving tax dollars. However, the current practice of estimating and assigning the ID value is not adequate and is frequently either overestimated or underestimated. This paper presents the development of a new model to accurately estimate ID value in highway rehabilitation projects based on the desired level of duration reduction. The model quantifies the impact of the I/D contracting method on the trade-off between time and cost of highway rehabilitation projects which should prove useful to SHAs in determining and assigning values of ID that are proper for the required project duration reduction together with the likelihood of achieving it.

A construction project requires collaboration of a large number of individuals working in different organizations to deliver the project on time and within the budget. Architecture, engineering, and construction industry spends time and money to solve the information sharing and interoperability issues between different domains. With the advent of building information modeling, a 3D CAD model of a project is used as a shared knowledge resource during the design, construction, and facility management phases of a project’s life cycle. Currently, 4D models are created by combining CAD and schedule information. A 4D model is useful for detecting potential construction conflicts during the project planning and design and a valuable tool during construction. However, creating 4D models requires a great amount of human involvement. An approach that facilitates integrating BIM and schedule information can improve efficiency and is highly desirable. This paper presents a new approach to information modeling using the Semantic Web technology. The Semantic Web provides an infrastructure and a data modeling format that allows computers to access and combine information distributed over the Internet. The paper discusses: (1) ontologies that the authors have developed for BIM and project schedule information; (2) how ontologies are used for creating project knowledge bases in RDF/OWL format; and (3) how project knowledge bases can facilitate machine processing of project information and semantic interoperability among project knowledge bases. The Semantic Web approach allows information to be saved once at the provider source and be used by authorized users as needed. This means BIM information is created and maintained by designers and project schedule is created and maintained by schedulers on their respective servers. This would allow the latest BIM and project schedule information to be accessed over the Internet and combined for various purposes.

Significant volatility in the price of asphalt cement is one of the most important challenges for both state departments of transportation (state DOTs) and highway contractors for proper cost estimating and budgeting of their projects. The ability to model and forecast asphalt cement prices can result in more accurate cost estimation and budgeting. However, there is little knowledge about how asphalt cement price fluctuates over time. The research objective of this paper is to model and forecast the price of asphalt cement using auto regressive conditional heteroscedasticity (ARCH) and generalized auto regressive conditional heteroscedasticity (GARCH) time series forecasting model which can model and predict both conditional mean and conditional variance of a variable. After analyzing the major characteristics (i.e., autocorrelation, stationarity, seasonality) of the time series of asphalt cement price, the primary conditional mean function is created using regular time series models such as auto-regressive moving average (ARMA). Then, by analyzing the residuals of this model, the conditional volatility of the price of asphalt cement is modeled using an ARCH/GARCH model. The results indicate that the developed model can predict the price of asphalt cement with less than 1.6% error.

In construction projects, problems regarding time overruns could be tackled by means of schedule crashing; however, this could be a very lengthy process if done manually. Normally, schedule crashing leads to cost overruns, so should be done in such a way that its negative effects are minimal on the cost of the project. This study developed a tool that applies an optimization technique to determine the best solution for schedule crashing problems. This tool requires a complete activity network as input, and calculates the best optimal solution for project schedule crashing. Several factors were considered while computing the optimized schedule, such as the overhead cost required to run the construction project, rewards in case of early completion, and penalties for late completion. Effective implementation of this tool could help to save time otherwise spent on calculating optimal solutions by using iterative methods. This tool offers flexibility in controlling the total project duration by a reasonable amount, thus reducing the negative effects of the change orders associated with time extensions.

Reliable cost estimates during the early phases of highway projects are essential to help stakeholders make an informed decision about whether to proceed with the project. Because most highway projects are sponsored by public agencies, the significance of preliminary cost estimates to the state highway agencies cannot be overstated. However, the conventionally used ordinary least square regression modeling approach has some serious drawbacks. This study employed ridge regression and a generalized feedforward neural network, which have never been used in building cost estimation models for highway projects. There are many advantages associated with those two methods. To show these advantages with the new approaches, an ordinary least square regression model was also developed. The identical data provided by the Florida Department of Transportation were used to develop cost estimation models for resurfacing projects using these techniques. After building the optimal models, this study assessed their performances basis on the same criteria, including root mean square error and root mean absolute error. The results showed that the generalized feedforward neural network model performed better than the ridge regression model. With sufficient data, these empirical modeling approaches can also be applied to develop prediction models for other types of highway projects.

It is a well-known fact that accurate estimation of the actual project duration has been difficult for the complex, dynamic construction projects. Statistics reveals that almost 80% of the construction projects were projected for delays that were estimated as above 30% of the initial duration. This wide variation in project planning and actual execution also causes repeated disruption in the flow of man-material-machine which results in inconsistent worker’s productivity. The inconsistency is more in case of workers employed on manpower intensive activities as against equipment intensive activities. Hence, the objective of the present study is to derive a realistic schedule that has insignificant deviation with the actual project progress at site. To achieve this objective, baseline productivity (BP) for manpower intensive activities was calculated using K-Mean clustering method and was used to determine the initial project duration. Five chosen critical factors such as imbalance in skilled-unskilled ratio, absenteeism, diversion of unskilled crew members on miscellaneous task, time loss due to late start and extended working hours that influence the labour characteristics were utilized to adjust the estimated project duration. These concepts were demonstrated and analysed with a case study.

Cost escalation is a critical issue that state departments of transportation (DOTs) face throughout project development from concept to completion. A variety of issues and risks impact budgeting and cost management and control activities, which affect adherence to approved project budgets. State DOTs need to utilize effective practices to avoid constant budget adjustments due to cost overruns. The objective of this study is to identify and analyze effective practices for controlling, updating, and maintaining the baseline budget and schedule for highway projects. Through comprehensive interviews with several state DOTs and an extensive content analysis of their guidebooks and manuals, we identify and analyze several effective practices, including those related to budget-based project development for controlling potential costs in the following areas of opportunity: (1) project scope and concept development; (2) concept-level risk identification and assessment; (3) preliminary and final design summary reporting; (4) value engineering and alternative analysis; and (5) cost estimate validation and review. Results of the analysis show that state DOTs struggle with identifying risk factors early in project development and mitigating them during preliminary and final design. The findings can help state DOTs improve their existing practices for highway project budgeting and cost management so that they can maintain initial baseline budgets and schedules while minimizing changes made to them during the project development process.

Recent work suggests replacing the unresolved construction scheduling question ‘who owns float’ by ‘how can float reduce the risk level across a project’. In other words, how to best distribute the time from project finish based just on productivities and the contractual finish? First, critical activities: they form the longest continuous path through a schedule and by definition are at risk. Delays to them will affect the performance of the entire project. This research assesses conceptual shortcomings of the related critical chain project management, and develops an algorithm that simulates how float can be allocated to activities by considering their individual risk. It quantifies how much sampled durations exceed their individual allocation. It is not intended to replace the experience of construction managers who empirically assign buffers to risky activities, but augment it so that an optimized float allocation can be determined for given activity risk. Allocation models build upon Penrose and examine exponents zero and one; ultimately validating the square root. Its contribution is an ability to quantify how schedules can be effectively protected.

Highway infrastructure projects are characterized by change and extra work orders which result from the uncertainty in the quantities of particular items. These items are specifically related to earthwork, where earth movement amounts are affected according to the excavation depth and soil type. The unit price contract is used to control discrepancies in quantities presented during construction, however, many of items can be precisely measured from the plans and specifications like traffics signs, concrete pavement and safety barriers. The hybrid bidding method called “CHICA” is a system which integrates a unit price contract with a lump sum contract to create a unique mixed contract. Setting unit prices for items with uncertain quantities and lump sum for items with precise known quantities, and incentives for early completion could produce reductions in extra costs, and reduce change and work orders, risks, and time. This research presents a comparison between the hybrids contracting method used in the second phase of construction of the highway PR-66, with the traditional unit price contracting method used in the first phase of construction of the same highway. The research demonstrates a significant reduction in costs, change orders, extra work orders, and time when using the hybrid contracting method in this particular situation which suggests that CHICA has great potential.

Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare-eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task. In this paper, a framework that automates the process is presented. Video data collected from the car’s parking camera is utilized to detect defects in frames. Simultaneously, elevation signals collected from accelerometers attached to the car are processed to reconstruct the profile of the road and detect defects associated with its z-axis, such as bumps. A GPS device is synchronized with the other sensors to acquire the data’s geolocation. Detected defects are then classified according to their type and their severity is assessed. All information is then transferred via 4G network to a central server, where the Road Condition Index of road segments necessary to classify roads is calculated. Finally, everything is saved in a Pavement Management System. Preliminary results on the processing of video data demonstrate the frameworks’ promising application. The initial identification of frames including defects produces an accuracy of 96% and approximately 97% precision. Further experiments on such frames, aiming at the detection of potholes, patches and three different types of cracks result in over 84% overall accuracy and over 85% precision.

This paper presents a comparison study of building measurements performed on 3D models generated by two different approaches. In one approach, the models were produced via close-range photogrammetry. Such models are based on still-frame photographs, post-processed with commercially available photogrammetric software. The second approach was the generation of 3D point-cloud models via laser scanning. For this purpose, a simple one-story structure was chosen. Students, from Georgia Southern University, established accurate benchmarks within a closed traverse to serve as standard reference points for all measurement comparisons. Several physical target points were then marked on the exterior walls of the structure. These target reference points were then measured with an accurate total-station instrument. After photographs were taken and laser scanning of the structure performed, the coordinates of these target reference points were also determined from the respective models. These coordinates were then compared with the ones obtained with the total-station instrument. Coordinates and distances from each procedure were compared to determine relative discrepancies and accuracies. The results of this study demonstrate that the photogrammetry approach can provide accurate information, comparable to measurements produced with total stations and laser scanners.

Given that labor is one of the most important resources in a construction project, collecting field data on workers’ activities (i.e., work sampling) is critical to understanding and managing workers’ performance for a productivity analysis. Unlike manual observation used for work sampling, automated action recognition and analysis using sensors, such as motion and image sensors, enable continuous worker monitoring and corresponding task assessment. Among diverse sensors, an accelerometer has great potential for automated action recognition due to its data richness and mobility. In this paper, we propose wrist-worn accelerometer-based action recognition with selected features and classifiers and apply it to masonry work to demonstrate its feasibility. The novelty of this approach is the use of a single affordable wrist-worn sensor, which would not interfere with workers’ ongoing work. The result shows that Multilayer Perceptron classifier can achieve about 97% of accuracy in posture classification in masonry work. The proposed approach has an immense potential to be used for non-intrusive action recognition for construction workers, which can open a door for diverse productivity analyses.

Airport pavements are constantly impacted by the heavy braking, and turning of aircraft that are major contributors to pavement failures such as surface shoving and slippage cracking; pavements are also greatly affected by high ambient and in-pavement temperatures during summer months. To detect airport pavement failures, the Federal Aviation Authority (FAA) implemented and installed strain gages in pavements at a few select major airports. This research focuses on results analyzed from strain gages installed by FAA that show pavement failures at the intersection of runway 4R-22L and High-Speed Taxiway N (HST-N) at Newark International Airport (EWR). Data from the pavement was collected by a data acquisition cabinet and transferred to a database for data analysis. The strain gage readings are described in technical terms, and the physical separation over time between the asphalt base layer and upper repaved layer is demonstrated. It is seen that non-destructive testing using embedded sensors can give warnings of pavement distresses that ultimately lead to failure. Statistical analysis using the Kolmogorov-Smirnov test and the differences between means test further confirm the pavement failures detected by the strain responses at EWR. The progression of pavement distress is described and evaluated.

In construction, workers are frequently exposed to manual handling tasks involving repetitive and forceful activities with awkward postures. As a result, construction workers are at about a fifty percent higher risk of work-related musculoskeletal disorders (WMSDs) than workers in other industries. Several ergonomic methods for evaluating workers’ postural stresses through observations (e.g., OWAS, PATH, or RULA) have been applied to identify the risks of WMSDs during occupational tasks including construction. While inexpensive and practical to use, these methods are constrained by time-consuming procedures, observer bias, and the need for a well-trained analyst. These issues are exacerbated in the context of construction. In contrast to other factory-based (e.g., manufacturing) industries, construction is subject to irregular work patterns, relatively longer cycle time, and a wide variety of tasks. To address these issues, we propose computer vision-based posture classification that can automate existing observation-based postural evaluation methods such as OWAS. Specifically, the proposed method can classify workers’ postures according to pre-defined ergonomic checklists using shape- and radial histogram-based features from video sequences. To test the feasibility of the proposed approach, we conducted a laboratory test with five subjects by simulating three representative postures according to upper limbs, back, and lower limbs. The results from the case study demonstrate that it can provide more reliable posture classification results with about 93% of accuracy. The proposed approach enables practitioners to identify the risk of WMSDs through automated ergonomic postural evaluation of on-going tasks only by taking videos at sites, which helps to prevent WMSDs for diverse occupational tasks including construction.

The free vibration of bridges and patterns in bridge-vehicle dynamic interactions can help signify decaying components of bridges and predict structural risks, such as scouring. Traditional methods—including contact sensors, Laser vibrometers, and videogrammetric algorithms—often require a time-consuming process of manual interpretation to identify anomalous vibration modes that imply underlying defects. Engineers can hardly examine all possible correlations between vibration modes and various scouring possibilities, because the number of combinations of vibration modes and possible scouring conditions is exponentially large. Using a bridge as an example, this paper examines an approach that automatically correlates the vibrations of bridge components captured in videos with potential scouring problems through an algorithm that automatically updates a numerical simulation model of the bridge based on video analyses. An algorithm then simulates various scenarios of scouring on Finite Element Analysis Model of the bridge, thereby determining the most likely scouring condition as those that produce similar vibrations extracted from videos. The authors tested this algorithm on a real bridge and found the actual length of the scour of a column by correlating the frequency extracted from the video data to that of frequency determined from FE analysis.

The most popular building information modeling (BIM) data schema, the industry foundation classes (IFC), does not cover all necessary concepts and relations for representing information for supporting automated compliance checking (ACC). The IFC schema needs to be extended to fully support ACC. Previous IFC extension efforts typically extended the IFC schema in a manual (which entailed an effort-intensive and largely subjective) manner. A more automated (thus, a less effort-intensive and more objective) method for extending the IFC is needed. To address this gap, a new method for finding concepts from domain documents to incorporate into the current IFC class hierarchy is proposed. In this new IFC extension method, machine learning (ML)-based relationship classification is used to predict the relationship between each pair of matched concepts. This paper focuses on presenting the ML-based relationship classification method and its testing results. In the proposed method, a supervised ML-based approach is taken to train a classifier to automatically identify the relationship (equivalent, super, sub, or associated concept) between a pair of concepts. Four main ML algorithms were tested, including Naïve Bayes, decision tree, k-nearest neighbors, and support vector machines. Eight syntactic and semantic features of concept pairs were used in training the classifiers. The performances of the different ML algorithms were compared based on their classification precision. The best performing algorithm achieved 88.2% precision.

Buildings, which account for the majority of electricity consumption in the US, lack efficient electricity monitoring tools for occupants to take action and reduce energy consumption. Non-intrusive load monitoring is a cost effective approach for electricity consumption disaggregation at the appliance level by using one sensing node in a building unit and leveraging specialized algorithms to identify appliances operations. A successful implementation depends on the algorithms’ performance. A key algorithmic component, in commonly used techniques, is event detection which identifies appliance states’ changes (e.g., going from on to off). The Generalized Likelihood Ratio (GLR) test is a common technique for event detection in high resolution data. This algorithm requires parameter tuning in each building unit to optimize its false positive rate. In this paper, the feasibility of improving event detection algorithms is explored by coupling it with unsupervised clustering to learn from the environment and tune the event detection algorithm. Hierarchical clustering is used as the base method. The feasibility study was conducted using the data collected from field experimental set up over two weeks and it was shown (on a limited data set) that context awareness event detection could potentially reduce unimportant detected events, up to one third of total detected events.

This paper presents a fast and scalable method for activity analysis of construction equipment involved in earthmoving operations from highly varying long-sequence videos obtained from fixed cameras. A common approach to characterize equipment activities consists of detecting and tracking the equipment within the video volume, recognizing interest points and describing them locally, and following by a bag-of-words representation for classifying activities. While successful results have been achieved in each aspect of detection, tracking, and activity recognition, the highly varying degree of intra-class variability in resources, occlusions and scene clutter, the difficulties in defining visually-distinct activities, together with long computational time have challenged scalability of current solutions. In this paper, we present a new end-to-end automated method to recognize the equipment activities by simultaneously detecting and tracking features, and characterizing the spatial kinematics of features via a decision tree. The method is tested on an unprecedented dataset of 5hr-long real-world videos of interacting pairs of excavators and trucks. The Experimental results show that the method is capable of activity recognition with accuracy of 88.91% with a computational time less than one-to-one ratio for each video length. The benefits of the proposed method for root-cause assessment of performance deviations are discussed.

A large number of deficient bridges may endanger the public and affect the economy at a broader scale. Bridge superstructure rating is a critical element that affects the overall sufficiency rating of a bridge. Accurately predicting the superstructure performance of a bridge may help agencies better prioritize their resources for maintenance and repairs. The main objective of the paper is to utilize data mining techniques to develop reliable models to predict the superstructure rating of bridges. This research utilizes the national bridge inventory (NBI) database as the main source of information. A focused subset was created based on the defined scope of the research: year built (≥ 1955), kind of material-design (prestressed concrete and steel), type of design (stringer/multi-beam or girder), and deck type (concrete cast-in-place). This paper takes three approaches for model development including linear regression, decision tree, and neural network. The best model was identified for each superstructure material through comparisons among different models. In addition, a discussion of individual variables and their contributions to predict superstructure rating was performed. The identified models provide insight into when a bridge superstructure needs maintenance and reconstruction.

Given their large energy footprints and the availability of building energy management systems, airports are uniquely positioned to take advantage of demand response (DR) programs. Although a baseline—the estimation of what the load would have been without load reduction—is essential to assess the performance of DR strategies, there has been very little published research on developing baselines for airports. Therefore, the research described in this paper aims to develop baseline models specially intended for airport facilities. Specifically, we propose piece-wise linear regression models for predicting electricity demand using time-of-week, temperature, and flight schedule information. For the given period of April and May, test results reveals that a model, which has trained over specific seasonal data with only time-of-week and temperature as inputs, has the best prediction performance. The number of passengers of departure flight schedules is shown to have a positive relationship to the load, but does not improve the model accuracy significantly. However, since this study is done for the spring season, when HVAC systems run the least, the results may not represent other seasons with high cooling or heating demand. Therefore, further studies should be carried out to conclude the potential of flight schedules in improving accuracies of energy prediction baselines.

Tracking and visualizing the status of work-in-progress on construction sites can minimize the gap between short-term and long-term planning and lower coordination costs. Over the past few years, there have been studies on leveraging appearance information in point clouds and 4D building information models (BIM) to automatically detect progress deviations. These methods rely on libraries of construction material images to train the underlying material recognition models. Areas of the images corresponding to BIM elements are extracted and tested with the trained material recognition model. While successful results have been achieved, these extracted images contain occlusions that cause some BIM elements to be misclassified. To improve the accuracy, this paper presents a method that removes occlusions prior to performing material recognition. By creating 3D depth maps and simple linear iterative clustering (SLIC) superpixels, occlusions presented in the images are removed. The presented method is validated with four case studies. The experimental result shows the improved accuracies compared against that of the previous method without occlusion removal.

The use of motion sensing technologies for ergonomic analysis of worker motions has gained increasing attention in construction. Using motion capture data enables extracting ergonomic assessment inputs more accurately than through a human observer. Accordingly, methods of collecting and analyzing human motion data have been developed to automate the ergonomic evaluation process for effective identification of ergonomic risk factors associated with manual operations. However, despite advancements in motion capture technologies, there is still inaccuracy associated with the resulting motion capture data, which leads to impreciseness of the output of the ergonomic assessment. This study investigates the impact that the imprecision of the motion capture data has on the results of ergonomic analysis, to evaluate the technical feasibility of a motion sensing approach to ergonomic analysis and to discuss the potential solutions by incorporating sensing errors into the motion analysis. Specifically, different possible sensing errors pertaining to a body joint location have been considered and the sensitivity of the errors on the results of ergonomic evaluation has been quantified. The results can be used to obtain an accurate and realistic adjustment for the results of ergonomic assessment based on the amount of error associated with any motion capture technology.

The demand for improving energy efficiency of existing buildings is rising, hence many construction firms are shifting their efforts from constructing new buildings to rehabilitating the existing ones. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors try to collect energy-related contextual information to examine if their performance is maintained as expected by the design. However, occupants still continue to face challenges in identifying and being aware of ‘where’ and ‘what’ energy problems are emerging or likely to emerge in their buildings for taking timely corrective actions. This paper proposes a new ubiquitous context-aware application for building energy diagnostics using thermal camera-equipped smartphones. The proposed mobile platform consists of: (1) thermography-based visual sensing and analytics for characterizing building energy problems; and (2) infrastructure-free (i.e., available in GPS or WLAN-denied environments) remote 3D localization of the problems by leveraging multi-modal visual and motion sensory data from smartphones. Experimental results on an instructional facility promise that the proposed method offers significant potentials for recording the location and severity of building energy problems. As an efficient practice in energy informatics, such near real-time feedback will improve occupants’ situational awareness of the as-is building conditions and facilitate associated sustainable retrofits.

Vision-based tracking for project related entities has attracted practitioners’ interests and attentions; it can provide beneficial data for productivity analysis and safety monitoring. Some studies on tracking workforce and equipment using video cameras placed onsite have proved the feasibility and efficiency of vision-based tracking methods. However, existing tracking techniques have difficulties in tracking objects when occlusions occur. This paper presents a visual tracking method based on particle filters to resolve this issue. The method includes two main stages, prediction, and update. Initially, the target object is located with a rectangular window, and particles are generated from a normal distribution. Then, particles are propagated, and the weight of each particle is determined by the observation likelihoods. Particles are resampled to localize the target object based on weights. In this way, the personnel or construction equipment can be traced. The jobsite of Roccabella residential project in Montreal was selected as the test bed. A high definition camera was placed onsite to record the construction activities. Then, the collected videos were used to evaluate the tracking performance of this method. The results indicated the method was effective to track the object of interest in the complex situation of occlusions.

Spatial sensing of built infrastructure is now a common practice within the AEC industry and results are commonly encapsulated in the form of dense point cloud data (PCD). PCD of built infrastructure might consist of millions of spatial points and it is well known that processing all these points is neither necessary nor computationally feasible. In addition, due to several reasons including hardware and/or software deficiencies, there might be several outliers that need to be removed from the PCD before further processing. As the result, cleaning and refining PCD is a paramount step in the process of spatial sensing and object-oriented modeling of built infrastructure scenes. This research work entails two parts: The first part provides an in-depth literature review on current states of practice and research on the concept of PCD cleaning. The second part presents the authors’ suggested framework for cleaning and refining PCD of built infrastructure. This prototype mainly consists of three major components: (1) removing outliers; (2) filling holes and gaps on surfaces of PCD; and (3) balancing the density of different areas of PCD based on a plane recognition approach. Several case studies are presented to demonstrate the efficiency of the proposed framework.

A big size of bridge data is being constantly generated—from a variety of distributed sources and in heterogeneous formats—in the form of national bridge inventory (NBI) data, national bridge elements (NBE) data, bridge inspection and maintenance reports, traffic data, etc. Big bridge data analytics open unprecedented opportunities to leverage this large amount of data for improved bridge deterioration prediction and understanding, and enhanced maintenance decision making. In this paper, the authors propose a big bridge data analytics framework that consists of: (1) semantic data/information extraction and integration, (2) semantic data/information analysis and mining, and (3) semantic data/information retrieval and visualization. The big size of the data, in addition to it being scattered and heterogeneity of meaning and format, adds to the challenge of data integration and analysis. As such, at the cornerstone of this framework is an ontology of bridge deterioration knowledge, which aims to facilitate the semantic integration and analysis of the data based on content and domain-specific meaning, and to bridge the terminology gap across different sources. This paper focuses on providing an overview of the framework for big bridge data analytics and presenting the proposed bridge deterioration ontology as the first step towards implementing this framework.

Thermal comfort of building occupants is a major criterion in evaluating the performance of building systems. It is also a dominant factor in designing and optimizing building’s operation. However, existing thermal comfort models, such as Finger’s model currently adopted by ASHRAE Standard 55, rely on factors that require bulky and expensive equipment to measure. This paper attempts to take a radically different approach towards measuring the thermal comfort of building occupants by leveraging the ever-increasing capacity and capability of mobile and wearable devices. Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture a number of important parameters that may be used to measure thermal comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. This research evaluates such opportunities by fusing traditional environmental sensing data streams with newly available wearable sensing information. Furthermore, it identifies challenges for using existing wearable devices and to developing new models to predict human thermal comfort. Findings from this exploratory study identify the inaccuracy of sensors in cellphones and wearable as a challenge, yet one which can be improved using customized wearables. The study also suggests there exists a high potential for developing new models to predict human thermal sensation using artificial neural networks and additional factors that can be individually, unobtrusively, and dynamically measured using wearables.

On-site worker observation is a fundamental task for a wide spectrum of construction applications such as safety behavior monitoring and productivity analysis. Vision-based action recognition techniques have been proposed to complement the time-consuming and labor-intensive tasks involved in manual observation. In construction, however, previous studies have mainly utilized an RGB-D sensor (e.g., Microsoft Kinect), the operating conditions of which (e.g., active ranges from 80 cm to 4 m, sensitivity to sun light) may hinder the application to actual construction jobsites. To address these issues, we propose a silhouette-based human action recognition method using a single video camera that has less operational constraint. In this framework, the human worker is localized and tracked throughout the monocular video, based on both spatial (i.e., contour of worker) and temporal changes (i.e., moving direction and speed over consecutive frames). Then human action models are learned with temporally adjacent frames and utilized to recognize similar actions in testing video by computing the similarity between the learned action model and newly computed model in a testing dataset. For performance evaluation, we carried out lab experiments, in which a video camera was installed 5–10 m from multiple human subjects. Results indicate that the proposed framework performs well (i.e., an accuracy of 90.68%) to capture predefined poses (e.g., walking, lifting, crawling) in image sequences. This study thus explores an automated means for worker monitoring which potentially helps understand and measure human motions without significant human effort.

Occupant behaviors contribute significantly to energy consumption and waste in buildings. Effective implementation of intervention strategies is the key to minimize energy wastes derived by occupant behaviors. In particular, intervention strategies should be personalized, compatible with the attributes of occupants, and based on real-time and predictive patterns of occupants’ behaviors. This paper presents a smart system for integrated sensing, simulation, and feedback of occupant behaviors that enables implementation of personalized interventions. The proposed system includes a sensor network composed of non-invasive and inexpensive distance, light, temperature, and plug-load sensors to capture the presence of occupants and energy expenditures in different regions of a building. The data is stored for real-time evaluation of energy use and waste analysis. The data is also used for calibration and parameter estimation of an occupant energy simulation model used to predict occupants’ behaviors and energy use projections. Both real-time and predicted data are stored in a database of a Smart Android Application. The application utilizes both antecedent and consequence intervention strategies to promote energy saving behaviors. Using robust machine learning algorithms, the application enables implementation of personalized intervention strategies based on the attributes and behaviors of each occupant. For example, the application enables providing feedback regarding the current and projected energy consumption by each occupant, personalized information-graphs of energy consumption, rating of energy performance for each occupant compared to other occupants, and personalized energy saving tips. The implementation of the smart system developed in this research introduces great opportunities for moving beyond the conventional one-size-fits-all approaches toward a more personalized approach for effective implementation of intervention strategies to reduce energy waste in buildings.

The topic of analytics has gained extensive attention during the past years to encourage informed, data oriented decision-making. Although not formally explored, the topic is nothing new to the construction industry. Extensive volume of data on patterns and trends are already being collected and used within the decision making process in the construction projects, at the construction companies, or in national/global policy making. As part of an extended research effort at Purdue University, this paper reports on a study to methodologically explore the process of creation of insights from data in the construction research. A protocol to study construction analytics was developed by integration of the definition of analytics into the knowledge pyramid. The protocol is expanded through established definitions of data categories. The proposed protocol is then validated through an inductive study based on a limited sample of research in construction. The outcome of the research is a validated protocol for structured review of the patterns of collection, process, and application of data. This protocol is currently being used as a foundation for the ongoing research within a more extensive sample of the construction research.

Monitoring the pose of the construction equipment is an essential prerequisite for determining safety and productivity of construction processes. In order to make construction sites safer, there is high demand for accurate and real-time motion tracking tools and methods to capture any movement of the equipment and its parts. Computer vision (CV) techniques are becoming more popular because of their lower cost of deployment and availability compared with other techniques. However, few CV methods have been focused on equipment part detection and pose estimation. This paper aims to propose a new method to detect the parts of the construction equipment that can be used to detect its pose. Using the concept of synthetic images for each equipment’s part (e.g., bucket, dipper, boom, and body), multiple detectors are trained for each part from different views and applied to recognize the parts. The synthetic images are generated by overlaying the images of the 3D model of the equipment on the real images of the construction sites as background.

In recent years, the digital world has experienced an explosion in the magnitude of data being captured and recorded in various industry fields. Accordingly, big data management has emerged to analyze and extract value out of the collected data. The traditional construction industry also experiences an increase in data generation and storage. However, its potential and ability for adopting big data techniques has not been adequately studied. This paper investigates the trends of utilizing big data techniques in the construction research community, which eventually will impact construction practice. The application of 26 popular big data analysis techniques in six different construction research areas (represented by 30 prestigious construction journals) was thoroughly reviewed. The results show that many of these techniques have already been used by researchers in this field although the frequencies of using each technique in construction research as well as individual research areas varied. Also, differences were identified between the overall trends of using big data techniques in construction research versus their use in all engineering research fields. While showing a great potential of applying big data techniques in studying construction-related issues, this research also identified existing gaps for the research community to fill in its future endeavor.

The hotel industry is among the highest consumers of energy of all building sectors. However, its energy consumption is largely dependant on hotel occupancy. The energy forecasting model has previously little considered occupancy in predictions. To supplement this research gap, interviews and data collections were conducted with a hostel in Singapore that is indicative of hotel owners’ perspectives and concerns. Next, an occupancy-based energy auditing model was developed to allow better understanding of the calibration process for energy forecasting. The results show the importance of incorporating occupancy in the energy audit model and also show the estimation accuracy within 5–7% compared with the actual result.

The energy use for materials and on-site construction assumes significance after we reach a threshold regarding building operational energy efficiency. Integration of energy use and emissions into construction planning and monitoring tools will enable planners to evaluate alternate construction schedules based on time, cost, productivity, and environment. Such integration requires a systematic understanding of technological and operational parameters that influence the construction processes. This study presents a conceptual framework to assess the energy use and emissions of on-site construction processes of high-rise buildings. Five major work items namely earthmoving, concreting, formwork, rebar bending and cutting, and masonry work are considered. Based on this, a generic framework is presented for integrating sustainability metrics into construction project planning.

Building integrated photovoltaics (BIPV) refers to photovoltaic or solar cells that are integrated into the building envelope to generate ‘free’ energy from sunshine. BIPV product development has been ongoing for the past 30 years, but their practical applications have been limited in China. Clients and end users lack knowledge of cost analysis, to make more accurate decisions on use of PV products. BIPV cost-benefit analysis should include not only initial costs but also ongoing maintenance and repair costs throughout the building life. Moreover, the scope and factors of cost-benefit analysis should be extended to include the community and environment aspects. In a cost-benefit analysis, various risk and opportunity factors such as electricity price, and government support should be considered. However, up until now, there have been limited studies that analyzed cost-benefit factors of BIPV from a supply chain perspective. Most previous studies focused on the Building Attached PV projects. This research aims to gain an in-depth understanding on the costs – benefits of BIPV. A real-life BIPV case drawn from China was studied by using the life cycle assessment (LCA) method. The outcome of this research will contribute to revenue projections to the industry by understanding the investment risk and benefit of using BIPV in buildings. The original contribution of this study is a lifecycle cost assessment model of BIPV projects which include comprehensive cost and benefit factors during the project lifespan.

The building overall satisfaction level can be favorably improved by enhancing residents’ thermal comfort, indoor air quality, and indoor lighting quality. Various studies have shown that lighting plays a critical role in improving indoor environmental quality and occupant satisfaction. Although the high interaction of lighting and satisfaction has been admitted, the proper methodology that could be employed in the design process is still a missing piece of puzzle. The amount of light and its distribution highly depends on the interior partitions location and exterior envelope design strategies. In order to obtain the most satisfying design, the impact of different strategies should be evaluated during early stages of design. Therefore, this paper presents a model, which is capable of evaluating the lighting satisfaction level of a single-family house. This model employs defined indoor lighting quality index (LQI) and quantified satisfaction model to evaluate building lighting overall satisfaction level. In addition, to illustrate the modeling process and demonstrate the use of developed model, a housing unit is analyzed as an example.

Given the growing interest in improving the energy efficiency of buildings, researchers have generated many predictive models of energy consumption. Most studies use physical and statistical tools, and regression models were shown to be the most practical and accurate. This paper compares the electricity consumption performance of leadership in energy and environmental design (LEED) certified buildings to similar non-LEED buildings in climate zone 2B. To test the hypothesis that electricity consumption is similar for these two types of facilities, the research method is based on developing a performance model for non-LEED buildings, and investigating whether this same model predicts the performance of LEED certified buildings. Electricity data was collected from the buildings, in addition to data for multiple variables including weather, time, and several building factors. The data is used to generate multiple regression models that predict electricity consumption with an accuracy of 90% or more. The results show that the differences between LEED and non-LEED residuals are not as large as anticipated. After discussing the possible reasons behind the observed results, the authors commend the U.S. Green Building Council (USGBC) in their latest move toward considering the actual performance of the building during the occupation phase, as opposed to just the intended performance as set during the design and construction stages, in the newest LEED rating system.

Sustainability as defined by U.S. Green Building Council (USGBC) can be achieved by addressing the three dimensions of environment, economic, and social. This research utilizes case study approach to explore how pre-fabrication strategies impact sustainability goals of construction projects by addressing these three aforementioned dimensions. As a common tool in lean practice, prefabrication has often been used as means to improve productivity and reduce material and labor wastage. Prefabrication of building components at an offsite controlled environment has a positive effect on occupational safety through reduced onsite hours and heavy lifting. Therefore it can be concluded that prefabrication also addresses the social dimension of sustainability. This paper presents a case study of a recently completed project, where prefabrication was used in construction of the building’s north façade and the traditional method was used in the construction of south façade, thus providing the ability to compare and analyze the comparative impact of prefabrication versus traditional construction on the goal of sustainability. The paper discusses in details the process involved in prefabrication, starting from decision making, all the way to execution, the procurement process, the use of building information modeling (BIM), and the perceived project performance and the sustainability results.

Buildings contribute 39% of U.S. carbon dioxide emissions. One way to decrease greenhouse gases is to consider building energy retrofit. In this case, owners need to invest in energy retrofit solutions which can be costly. The objective of this research is to introduce a decision methodology to allocate retrofitting investments considering a group of buildings to minimize the total life-cycle costs. The proposed approach employs energy features and other characteristics of different buildings located in the area of study and identifies the most cost-efficient retrofitting strategy. The results map the best way to allocate investments to retrofit a group of buildings. The results can be used by air emission reduction and energy retrofit programs for effective use of limited funds and incentives for building retrofits by for instance identifying the communities with the most number of potential buildings to retrofit.

The building construction sector continues to be a major consumer of energy in the United States. Energy consumption in a building typically spans across various life-cycle phases such as the material production and constriction phase, use and maintenance phase, and the end-of-life phase, with the use and maintenance phase constituting around 80% of total energy requirements. Since people spend most of their time indoors, the energy use behavior of building occupants is a critical factor that affects the total energy consumption. In this paper, an agent based model which simulates the effects of this dynamic occupant behavior is coupled with an energy use simulation model through a distributed computing environment based on the principles defined in the lightweight communications and marshalling (LCM), a common tool employed in robotics. The framework addresses two main objectives: (1) achieve multimodal synchronization and data exchange of distinct and spatially distributed simulation models and (2) visualize the effect of dynamic occupant behavior on the life cycle energy consumption in a building. This framework will achieve data synchronization and transfer of information between different software modules, which run simultaneously in a distributed fashion thereby testing the effects of various occupancy related energy interventions on life-cycle energy consumption.

Green housing retrofits for increasing energy efficiency provide a multitude of benefits which include savings for homeowners in energy bills and curbing carbon emissions. The literature about the impact of housing retrofits on the economy and the environment is quite rich; however the measurement of social impacts of housing retrofits is less well developed. Social impacts of housing retrofits can be indicated in different ways, from the improvement of occupants’ health and comfort to providing job opportunities in the community. This study points to the need for considering possible social benefits of housing retrofits in addition to the ecological and economic benefits. However, the typical non-market value of social aspects makes it difficult to quantify the impacts of such aspects in engineering decisions and integrating them with economic and environmental aspects. The objective of this paper is to (1) identify social indicators of green housing retrofits; and (2) introduce an approach for valuing and assessing the identified indicators of a housing retrofit project. In this paper, a framework of social criteria is presented for a housing retrofit project. A prototype survey is presented for identifying and assessing social indicators of housing retrofits and preliminary results of a pilot study are also evaluated. Future directions on the evaluation of social aspects in green housing retrofits are discussed. The results of this study lay out the groundwork for the consideration of social aspects in green house retrofits and can help decision makers to measure the performance and improvements of social criteria in a housing retrofit project.

Significant reductions in the cost of solar photovoltaic modules have been coupled with the emergence of innovative financial tools and resulted in dramatic increases in the development of distributed renewable energy generation systems. The deep penetration of solar energy throughout our nation’s electric sector is dependent on successfully integrating distributed generation, storage, and demand management systems. Achieving this challenge will require improvements to component technologies coupled with paradigm shifts in how energy systems and controls are conceived, developed, tested, and deployed in the marketplace. An integrative approach to meet this challenge is presented including the formation of a collaboration platform for academic and industry partners that is coordinated around a physical smart grid test bed embedded in a large unregulated electric grid. The value adding processes of this ecosystem for research, education, and market deployment are described based on two years of operation. Specific strategies for the advancement of smart grid solutions leading to the deep penetration of renewable energy are also presented including topics of distributed generation, energy efficiency, advanced metering and controls, and cyber security. Implications of these strategies on research, education, and workforce development are also described.

While the recycling of asphalt shingles is beneficial in reducing the consumption of virgin materials, road performance should not be compromised. One major concern with recycled asphalt shingles (RAS) relates to the variability in the properties of the recycled materials originating from different sources. In addition, the rheological properties of RAS have not been evaluated as well as its influence on the virgin binder when used in asphalt mixtures. Therefore, the objectives of this study were to characterize the rheological properties and molecular fractions of RAS materials sampled from different sources across the country. In addition, the influence of RAS on the Superpave performance grade (PG) of the binder in the mix was investigated. Results of the experimental program indicated that the asphalt cement (AC) content in tear-off shingles was consistent among different RAS sources across the country. However, AC content in manufacturer waste shingles was noticeably lower than in tear-off shingles. In addition, all extracted RAS binders were graded as PG 118 + - xx using the Superpave binder specification system. This stiff behavior is due to the binder used in shingle manufacturing, which is an air-blown asphalt binder with stiff characteristics and low elongation properties. Results showed that at a RAS content of up to 5%, the high temperature grade of the blends was increased by one to seven grades and the low temperature grade was reduced by one grade. The use of binder blending charts is recommended to account for the influence of RAS in the mix design. At a RAS content of 10%, the binder blends did not pass the Superpave criterion at low temperature.

Occupants’ satisfaction is becoming a more important issue in our modern competitive work system. Satisfaction is largely a state of mind depending on many different parameters. There are various strong evidences indicating that building characteristics and the quality of indoor environment significantly influence the level of satisfaction and efficiency of occupants in commercial buildings. In order to increase the overall satisfaction level, decision makers should face the challenge to identify and apply those strategies that have the highest probability to satisfy building occupants. While the benefits of occupants’ satisfaction improvement are acknowledged, the impacts of different strategies and their expected results have not been quantitatively described. This paper presents a methodology for developing a quantitative model to measure the overall satisfaction of occupants in commercial buildings associated with indoor environment quality such as lighting, temperature satisfaction, etc. A survey is design to collect the associated data. In addition, three different models including simple multi-variate regression model, model based on the normalized averages of weightings, and refined normalized weightings have been developed to quantify the overall satisfaction of occupants. These models will help to identify the most influential factors affecting their satisfaction. In addition, a probability distribution of weighing for each aspect through the studied population is presented which will be a beneficial tool to estimate the outcomes of different strategies.

Lighting systems account for approximately 50–70% of the total electricity consumption in commercial buildings. Previous research has shown that occupant behavior and their interactions with building systems significantly influence the total energy consumption in buildings. Yet neither architects/engineers design user-centered buildings that take into account these preferences nor do facility managers operate building’s systems based on the end-users needs and preferences. Meanwhile, studies have shown day-lit environments could provide up to a 50% reduction in the electricity consumption associated with artificial lighting and increase occupant productivity, comfort, and well-being. In this paper, we present a user-centered approach where buildings can be designed according to the occupants’ preferences and operated based on their behavior as well as their interactions with building systems. Through a presented case study, we have used the data collected from two experimental studies, (1) participants’ lighting preferences and (2) participants’ interactions with lighting systems in different default settings, to improve the design of an office space according to the participant’s preferences and provide operational strategies in order to reduce occupants’ interactions with such systems. The presented approach provides a methodology that can initially help architects/engineers design more energy efficient buildings around the occupants’ preferences and facility managers operate the buildings systems according to the occupant behavior.

Energy consumption of residential buildings is influenced by different factors. There is currently a considerable number of research attempting to understand the effects of such factors on energy performance of residential built environment. However, there is still a need for comprehensive studies reviewing influencing factors. The objective of this paper is therefore to present a detailed classification of the factors influencing energy consumption by reviewing the most recent publications employed in the pursuit of identifying and investigating such factors. Building’s physical characteristics, appliances’ and systems’ characteristics, occupants’ energy behavior characteristics, and climate characteristics are the four main categories of factors. This review also presents and discusses current lack points existing in the literature. Clarifying the boundary of research, determining the time of data collection step, considering all types of energy consumption units, and understanding the indirect effects of factors on energy consumption are the main points dismissed in most literature. Finally, this study puts perspectives for the ongoing research aiming to work on energy related factors.

Sustainability is assessed using various performance measures with respect to three main dimensions, namely economic, environmental and social. Energy consumption, a principal performance indicator of sustainability, is a major cost in operating buildings. Hence, optimizing energy usage has become a major concern in sustainability since buildings needs can be up to 45% of global energy demand. A clear example is retail store buildings, where most energy is expended in space heating and lighting. Lighting retail stores requires large number of different types of bulbs that are used for long hours. Hence, there is a need to conduct a study to minimize the energy used for lighting in retail stores while satisfying design and sustainability requirements. This paper presents a framework for an optimization model for the light design in retail store. The objective is to minimize electricity consumption. The decision variables for this optimization model were defined from the literature, light design codes, and the LEED requirements for energy and atmosphere. The paper presents (1) the problem statement and methodology of work; (2) the proposed optimization model; and (3) a list of all decision variables and their significance. The proposed model is expected to be a valuable tool for minimizing energy consumption in retail stores.

The restoration or rehabilitation of timber structures requires that the structural system be analyzed for determining load-bearing capacity. This analysis requires that properties of both the members and the connections be determined for accurate modelling. A number of non-destructive approaches have been developed to determine the structural properties of the wood. To date, little progress has been made in determining the structural properties of timber connections. A method based on analyzing the frequency of vibration of the member is presented. The theory, lab validation, and application to six historic structures are shown. The results from field testing provided a number of significant findings: First, the wide-spread assumption that timber joints have insignificant rotational stiffness (α < 1) is not valid. 70% of the 20 joints tested had stiffness values that classified them as semi-rigid (1 < α < 100). Second, the finding from lab work was confirmed: the ability to accurately predict the stiffness improves as the joint stiffness increases from pinned to semi-rigid (Percent error of 84% for α<1, and 36% for 1< α <100). The results of the sample analysis using this information found that calculated stresses in members were reduced by up to 21%.

With an increasing consumer demand for environmentally friendly building products and materials, sustainability is becoming a paramount concern to key stakeholders in the construction industry. Insulation materials used in homes and commercial buildings play a primary role in their overall energy efficiency, and the production and disposal of the voluminous amount of insulation materials poses a significant environmental challenge. In this context, this study utilized a proposed sustainable design process to investigate the metrics associated with an alternative insulation for use in buildings. A prototype exploring the use of evacuated packets of a silica compound substituting for conventional insulation was assessed. Assessment criteria included experimental comparison of heat transfer characteristics, as compared to a control facility. Additionally, a comprehensive environmental life cycle assessment and disassembly analysis was performed. Pilot study results indicate that in the new insulation design applied to the unit, heat flux decreased by an average of 4%, and improved environmental sustainability, resulting in a savings of 1.2 metric tons of CO₂e over 20 years per 100 ft² of insulation replaced. Results provide an alternative insulation design for use in construction, and a framework by which to assess the efficiency and environmental performance of sustainable building products.

Transportation authorities and industrial organizations are currently faced with the need to incorporate greenhouse gas (GHG) impacts into pavement designs. Previous studies have evaluated GHG emissions from individual projects with unique external environment. The conclusions drawn from previous projects cannot be applied to future projects in which the external environment changes. In this paper, we develop a GHG estimation method to assist in low-carbon design for future pavement construction. The key of this method was using mechanistic-empirical pavement design (MEPDG) to create comparable structures with different pavement techniques, traffic volumes, climate conditions, etc. The method was illustrated on the intercounty connector (ICC), which was paved with hot mix asphalt (HMA). The result show that, if the ICC’s base layer was paved with foam stabilized base (FSB), GHG emissions would be reduced by up to 2.1 thousand metric tons, an equivalent to 54.2% of the construction emissions. The GHG savings would generate 166 thousand dollars of carbon credits, which compensate approximately 2.7% of the pavement costs.

Many mechanisms exist to influence the adoption of appropriate green building innovations, from policy mandates and incentives to education campaigns and product trials. However, not all programs are equally effective in achieving the goal of promoting adoption of innovations, and sometimes the adoption of an innovation can have unanticipated consequences that negatively influence the potential for diffusion of the innovation to other stakeholders. Given constrained resources, how can designers of sustainability programs most effectively promote the adoption of green building innovations that achieve desired resource conservation goals without introducing other problems such as public health threats or consumption rebound effects? This study employed a process of stakeholder interaction mapping to identify possible points of influence for changing peoples’ decisions about specific water-related innovations in green buildings, including water heating and conservation technologies. The study found that existing tools intended to improve technology selection decisions may be targeted toward end-user stakeholders with comparatively lower degrees of control over actual technology selection decisions. The study also identified other key supply chain factors that may influence the availability of specific technologies through normal channels, thereby reducing the pool of feasible options in making selection decisions. Study findings suggest a need to consider a whole-systems perspective when choosing points to introduce program interventions. Designers of programs should also systematically identify supply chain and procurement constraints that affect feasibility of options when providing information to support stakeholder decision-making.

Recent technological advancements have enabled energy efficiency building through automation, equipment upgrade, and material improvement. These enhancements are mostly focused on centrally controllable units such as HVAC, lighting systems, and equipment, while miscellaneous energy loads (e.g., plug loads) that are mostly controlled by occupants, are not as efficiently managed. Various energy rating systems, such as LEED, grade the mechanical and electrical equipment in buildings. However, the fact that people spend more than 90% of their time indoors, suggests a great opportunity to help buildings reach a higher level of efficiency by monitoring and quantifying occupants’ energy-use. In this paper, we review the existing literature on occupant energy efficiency programs in residential and commercial buildings, and benchmark the potential of such programs to impact LEED rating systems. The main contribution of this paper is comparing the current occupant energy efficiency programs’ potential against standard industry measures (i.e., LEED). The results report a possible potential impact for residential building occupant energy efficiency, while indicating the lack of adequate reference and reliable measurement for the commercial sector. The details of the methodology, data analysis, and results are discussed in the paper along with future avenues of occupant-oriented energy efficiency research.

This paper applies, extends, and verifies a model-driven framework for managing carbon footprint of a construction project using a case study approach. Current construction management practice lacks methods to track carbon footprint of the construction phase since the application of life cycle assessment (LCA) methods has remained primarily limited to design and operation phases of a project. In addition, the current methods heavily rely on user inputs on materials and the state of planned-vs.-actual work-in-progress. The presented framework addresses existing inefficiencies by leveraging 4D building information models (BIM) in conjunction with LCA tools to benchmark carbon footprint during pre-construction phases and monitor it during the construction phase. The management framework—built on earned value management concepts—offers metrics to assess and communicate deviations between benchmarked and actual carbon footprint, and facilitates root-cause assessments to minimize performance deviations, and excessive carbon footprints. As a full proof-of-concept, an extended case study is conducted on a real-world project. The application of the proposed management framework as a mechanism for increased environmental product data in the supply chain, establishing policies requiring adherence to a carbon footprint budget during project tendering, and incentivizing reductions in carbon footprint through a cap-and-trade scheme are discussed in detail.

The net zero energy house (NZEH) is defined as a house with greatly reduced energy needs which can be supplied with renewable energy technologies. Renewable energy (RE) produces less greenhouse gas emissions compared to fossil fuels, and it is more cost effective. The objective of this study is to understand the energy performance of an existing NZEH house. The GridStar house—a NZEH located in the Navy yard, Philadelphia—is selected as a case study. This objective is achieved by creating an energy simulation model for the NZEH using DesignBuilder software. Then, the daily, monthly, and annually energy performance of the house was investigated using EnergyPlus software. In addition, the interaction between the NZEH with the grid is studied in this paper. The results of this study shows that the Renewable Energy System (RES) used in the GridStar house was able to offset 83% of the total energy demand of the house and the energy efficient envelope with the energy efficient HVAC system used in the GridStar house helped decrease the energy consumption of the house by 42% compared to a typical residential house.

As historic projects grow in public appeal and become more commonplace, opportunities for involvement are increasing for the construction industry. This paper explores the different perceptions regarding the benefits, challenges, and motivating factors that exist between the different parties associated with historic construction projects. The results support the idea that the construction industry should be interested in, and a proponent of, historic projects and that, likewise, historic preservationists should take advantage of the skills and abilities found within the construction industry to benefit historic properties. Understanding the mutual benefits for the different parties, and especially how additional benefits can be reached, will serve to nurture further partnership between preservation and construction. The common motivating interests identified in this research included intrinsic motivation and satisfaction, increasing market interest, publicity and creating niches, and sustainability. Challenges common to the different groups included unforeseen conditions, balancing project priorities, finding a qualified project team and craft, working with existing conditions, and scope creep. Finally, the recommendations generated through this study included setting a clear vision, conducting sufficient initial research, generating accurate answers to preliminary questions, understanding basic standards, codes, and practices, and establishing a unified team relationship.

Commercial buildings account for 18% of the total annual energy use in the United States, which makes energy conservation efforts in office environments crucial to meeting future energy-reduction goals. As existing building stock renews and future buildings become “smarter,” occupant-driven loads such as plug loads become more important in regulating energy use. Previous studies have focused on adopting sensors and informatics to benchmark and reduce plug load usage and modeled the impacts of various energy interventions on occupant loads by using standard occupant schedules over relatively short periods of time. However, wireless network disruptions and manual plug load data management for technology driven interventions, variability of individual occupant schedules, and lack of occupant engagement have all been identified as challenges to both accurately quantifying and further reducing the energy use on campus. In this paper, we discuss the aforementioned challenges in the light of our experiences in installing plug load management systems to 86 work desks in a high-performance office building on the University of Washington campus. The installation process is discussed both from occupants’ and research team’s point of view. As a conclusion, we propose a set of recommendations for the technical realization of future energy intervention studies.

The development of tools that provide feedback in the design process of energy efficient housing can have a positive impact in the reduction of costs associated with design. These tools might incentivize the implementation of sustainable practices during construction and retrofitting of new and existing houses. The challenge for this situation is to find a method that allows the use of existing detailed simulation tools, starting at early design stages when some technical sub-systems variables of the house are not yet available. This paper proposes the use of system dynamics (SD) as a methodology for the integration of different simulations that take place during the design of energy efficient housing propositions and the cost estimate associated with the selection of materials for construction. A literature review was conducted in order to identify similar uses of SD in the fields of civil engineering and architecture. The current research presents previous uses of SD as well as a model that integrates simulation tools for the design, analysis and cost estimate of energy efficient housing. A future research plan is laid out and the expected results are discussed.

In the United States, over 60% of the housing inventory is more than 30 years old. One way to improve energy efficiency of those aged buildings is through housing retrofits. One of the main challenges of housing retrofit projects is making the decision about the amount of investment that results in maximum long-time benefits. In terms of life-cycle cost for a housing retrofit, different factors may affect the type of retrofitting alternatives to be implemented in the project. This research first introduces an optimization model for decision-making in housing retrofit. The model incorporates the use of genetic algorithm for selecting the optimum retrofitting plan based on the minimum life-cycle cost of the building. Then using a case study through this research a sensitivity analysis is performed to evaluate the impact of different factors such as service life of the building, homeowner’s available budget, and discount rate of the building location on the suggested optimum retrofitting alternatives. The initial results illustrate that the retrofitting efforts could be more feasible when the service life of the building is high and the amount of discount rate is low. The results can help homeowners to make a more accurate decision for a housing energy retrofit.

Sustainability assessment methods can vary widely across different scales. Existing sustainability assessment systems, however, are usually focused on a single scale. To address this gap, in this paper, the authors propose a new framework for big-data-intensive multi-scale sustainability assessment and prediction at: the building scale, neighborhood scale, and city scale. This paper focuses on presenting the system of sustainability indicators that can support such multi-scale analysis. First, the paper summarizes the literature review of current sustainability indicator systems and identifies the knowledge gaps in supporting multi-scale sustainability analysis. Second, the paper presents the methodology and results of the comparative analysis of existing indicator systems, which was conducted based on a set of well-defined criteria. Third, the paper presents the proposed multi-scale sustainability indicator system, including the methodology of development, the proposed set of indicators, and an initial verification of coverage based on a comparison with existing indicator systems.

Location and transportation (LT) is a new category in the United States Green Building Council’s leadership in energy and environmental design (LEED) rating system. The location and transportation category emphasizes walkability and includes strategies to reduce resources related to transporting people. LEED recognizes LT as a critical opportunity to reduce impacts on costs, pollution, depletion of resources, and to influence people within the built environment based on a building’s close proximity to amenities and alternative transportation options. In addition, the LT strategies aim to increase the health and welfare of building occupants by promoting walkability. The research that follows focuses on how a building’s location in terms of surrounding density and diverse uses relates to the health aspects of increased walkability rather than automobile use. Decreased dependencies on automobiles have been thought to correlate to a decrease in the body mass index (BMI) of individuals. More specifically, this research utilizes a cross-sectional survey to collect data on BMI and automobile use from residents of an urban infill housing project in downtown Los Angeles, California. The data was collected during the winter of 2015, with BMI being the dependent variable and automobile use as an independent variable.

Lessons learned from the fourth Michael Horman Symposium, a cross-institution research methods symposium, are described in this paper. The symposium provides a forum for undergraduate and graduate students to discuss their research ideas and develop a network of mentors as they conduct their studies. The aims of the symposium are to introduce innovative qualitative and quantitative research methods, to improve research communication, and to involve a diverse group of faculty mentors from several universities in the U.S. Distinctive approaches of the symposium are the inclusion of team-based activities across multiple schools and student levels, and informal learning settings. Approximately 30 students participated in the symposium by sharing their experiences and reflecting on a series of questions including: how do you conceptualize research methods? Why are methods important for research? And how do you determine which method(s) is/are appropriate for your research topic? The students worked in small groups to answer these questions. The preliminary findings suggest that novice researchers experience confusion, challenges when evaluating, and selecting research methods to support their investigation. This matter needs to be addressed by future interventions informed by research. Nevertheless, the symposium resulted in significant pedagogical, methodological, and developmental outcomes for participants.

Human behavior has been well recognized to have a significant influence on energy consumption both indoors and outdoors. Traditionally, these behaviors have been investigated separately as occupant behavior and travel behavior. However, as human activity patterns are interconnected and our choices as travelers or occupants vary spatially and temporally, there is a need to understand the link between travel and occupant behavior and how they are affected by various factors both directly and indirectly. This paper presents a methodology that aims to identify the possible linkages between travel and occupant behavior by combining individually measured electricity consumption data with survey data on travel attitudes. Through the proposed methodology, we aim to investigate how a long-term energy intervention in the office environment affects occupants’ attitudes towards energy-efficient travel. A study applying the methodology will be started in the University of Washington campus in October 2015.

Construction has far reaching impacts on human society, economics, and the environment due to its pervasiveness in society, investment of financial resources, and commitment of natural resources. Sustainable development is contingent on reaching a balance between these three elements, often referred to as the triple bottom line. Sustainability rating systems have been developed for the built environment as tools to aid in the growth and application of sustainable development. Examples of rating systems for buildings include building research establishment’s environmental assessment method (BREEAM) and the U.S. Green Building Council’s Leadership in Energy and Environmental Design (LEED). Examples of rating systems for infrastructure include Civil Engineering Environmental Quality Assessment and Award Scheme (CEEQUAL) and the Institute for Sustainable Infrastructure’s (ISI) Envision. However, there is no widely accepted method to determine the effectiveness of the more than 100 sustainable rating systems in existence or the future rating systems to be developed. A framework is needed to specify criteria for assessing sustainable rating systems. This paper consists of the first phase in developing this framework: identifying its key assessment criteria. The paper reviews 92 published sources on sustainable development across multiple disciplines to identify and summarize the 19 key criteria used to assess sustainability rating systems. The second phase of this research effort will consist of using these 19 criteria to develop the framework, and apply it to assess, improve, and compare sustainable rating systems, while also providing an approach to create new rating systems.

Home energy auditing is a focused inspection service in the building industry that seeks to identify and motivate investments in energy efficiency improvements. In many cases, the audit process does not translate into actions by homeowners (Palmer 2013). Barriers to action by homeowners include a lack of information, financial concerns, and a lack of trust between auditors and homeowners (2009 CEQ Report). The goal of this research is to characterize the trust relationships among home audit processes, the accumulation of trust, and the intention to take action based on auditor interaction with homeowners. The relationships between trust attributes and implementation of energy improvements are presented as performance metrics to measure value exchange during the audit process. The designs of a classification framework and the questionnaire instrument to evaluate trust-value exchange are also presented. The instruments are used for multiple audit delivery processes to characterize the relationships between the trust-value exchange during audits and the intention to invest in home improvement projects. To assess this exchange, typical value-adding processes that take place during audits are characterized based on observation of audits under various circumstances. The contributions of this study to the enhancement of business development and building characterization processes are discussed.

Sustainability of urban energy systems is among the main concerns of planners. Improving energy efficiency is a major sustainability issue considering the population growth, limited energy resources and global climate change. Energy mapping enables decision makers and planners to visualize and evaluate the spatial patterns of energy consumption and to analyze future scenarios for improving energy performance (EP), such as renovation alternatives. The main input for energy mapping is the measured or estimated energy consumption of each building in a specific area. Energy meters are the preferable source to provide these data in an accurate and simple way. However, getting access to these data from utility companies in a disaggregated format is usually difficult, mainly because of privacy concerns. Therefore, it is often necessary to estimate the energy consumption of buildings using simulation combined with aggregated metering data. This paper will review recent approaches to develop energy mapping at the urban scale, as well as simplified energy simulation tools and computer modeling tools and formats including geographical information systems (GIS), CityGML and building information modelling (BIM). A case study, focusing on the energy map of Concordia University SGW Campus, is provided to demonstrate some of the reviewed approaches.

The success of international construction businesses is highly subject to market uncertainties in association with host country attributes. That’s because going international requires a large-scale investment and target country selection is a key component of corporate decision making. However, despite emerging interests and the need for systematic country evaluation, most existing studies have focused on specific regions or countries and have lacked comparative analyses among diverse countries using objective information. To address this limitation, this study suggests a data-driven approach to country classification using a two-stage model. The first stage represents a process of macro-classification to screen negative countries regarding country attractiveness. The second stage is a micro-classification procedure whereby promising countries are identified based on their level of past project performance. The model utilizes not only secondary data from internationally reputable institutions but also actual performance data of Korean firms from 1990 to 2014 to evaluate 32 countries. Using a factor analysis and two-step cluster analyses, the model allows us to cluster 32 countries by characteristics and explain the current status of each country. The proposed approach enables firms to quantitatively evaluate candidate countries and make better decisions at the early stage of corporate strategy development.

Past research shows that Hofstede’s cultural dimension of uncertainty avoidance explains variance in nations’ technology choice for sanitation and electricity infrastructure construction. The uncertainty avoidance dimension describes the way that nations deal with ambiguity and uncertainty. This paper is part of a larger project that links that previous national scale research to the project level that is most relevant to the construction practice. As such, this paper reviews methods from the literature that measure individual risk attitudes, including issues of measurement and risk determinants. For example, this paper discusses paid real-stakes lotteries, general risk questions, and context specific risk questions. Respondent gender, age, and income are identified as determinants of risky behavior. The utility of these various measurement strategies is discussed with specific regard to future research intended to explain variance in the construction of distributed household renewable electricity infrastructure. Finally, a questionnaire design for future research is proposed.

In the last few decades, global warming and climate change have had great impact on infrastructures. Increasingly frequent extreme weather conditions, such as heat wave, severe cold, floods, and earthquakes, significantly change the construction and operation process of infrastructures. Huge social and economic lost raises the awareness to address and alleviate possible risks resulted from climate change. Proper assessment approach is needed to appropriately evaluate the added risks from climate change. This research discusses the features of climate change risk assessment, and Bayesian Networks method is proven to be an effective tool to construct the assessment model due to its features. This paper proposes to develop a Bayesian Networks-based risk assessment framework to evaluate the impact of climate change on the infrastructures. The risk assessment steps are presented using a case study of high speed railway operation.

This paper compares the robustness of two different meta-heuristic algorithms on multi-objective optimization. This was done on a highway maintenance problem for simultaneously optimizing four conflicting objectives of time, cost, level of service, and environmental impact, in addition to a construction time-cost tradeoff (TCT) problem. These previously developed frameworks respectively used ant colony optimization (ACO) and genetic algorithm (GA) with adaptive weights. By the aim of constructing the comparison methodology, the optimization functions for each case study were restructured and solved using a GA based optimization software (Evolver™). The comparison of the optimization results for the two previously developed algorithms with the new approach reveals that the presented methodology requires less computational time and can generate better optimal solutions.

Ground transportation systems are essential for the mobility of people, goods, and services. Thus, making sure these systems are resilient to the impact of disasters has become a top priority for policy makers. Currently, one of the major obstacles for increasing the resilience of critical infrastructure is the lack of a measuring framework that can assess the condition of the system and aid in the design of strategic plans and policies. This research addresses this problem by evaluating two existing frameworks for quantitatively assessing the resilience of general infrastructure systems. A comparison of the two frameworks is made, pointing out the strengths and weakness of each when applied to ground transportation systems. The results show that while both frameworks apply to roadway systems, the three-stage framework of Ouyang, Dueñas-Osorio and Min is the most appropriate due to its capability of measuring the level to which the resilience performance standards are met. This work sets a foundation for future development of traffic-based resilience measuring models.

Identifying the most suitable project alternative is a critical challenge faced by decision makers in the front-end phase of major transportation projects (MTPs). The increasing complexity and dynamism of MTPs impose substantial uncertainties and subjectivity during the front-end phase of such projects. The methods commonly used during the front-end phase of MTPs are mainly deterministic. The objective of this research is: (1) to investigate the decision-making criteria for such projects through literature review and establish the state-of-the practice on the subject matter by conducting a questionnaire survey and (2) to build a conceptual stochastic decision-support framework to address the considerable uncertainties in MTPs based on the literature review and survey findings.

The implementation of diverse software applications through the highway project delivery imposes big challenges for integrating digital life-cycle data to support decision making due to the potential inconsistencies of levels of detail, data syntax, and semantics. This paper presents an ontology based exchange framework that enables the unification and interconnection of separate life-cycle data spaces. The framework employs resources description framework (RDF) as the means for storing and connecting isolated project data spaces. Three separate ontologies for design product, construction event, and condition survey event were developed to support data translation from proprietary formats to RDF format, and a merged ontology is also built to guide the connection of distinct RDF graphs.

Wayfinding is a fundamental cognitive function that helps people maintain a sense of location and direction. On one hand, portable global positioning systems (GPS) have significantly facilitated our outdoor wayfinding; on the other hand, there hasn’t been a widely implemented indoor wayfinding solution to adequately address our navigation and evacuation concerns. For example, conventional signage systems can be hard to notice and have been criticized for their incapability of conveying comprehensive information. Mobile devices have inherent limitations such as heavy reliance on infrastructure and requiring mental effort to process information from the screen. This paper presents a framework of color-coded indoor wayfinding assistance. This framework uses different color themes to facilitate daily navigation and aid in emergency evacuation. Within each theme, color transitions are designed to indicate the direction of exit. Related research topics of applying such a framework for indoor wayfinding are discussed, including the design of color codes, the psychological effects of colors, and the possibility of using Virtual Reality as an experimental tool. Lastly, a plan for a next-step experiment is introduced and opened for discussion.

Research shows that flood risk assessment methodologies incorporating economic, environmental and societal impacts are rare and flooding mitigation alternatives (FMAs) are mostly compared considering only economic criteria. In this study, a probabilistic spatial flood risk assessment methodology (PSFRA), capable of integrating the economic, social, and environmental impacts of floods within the watershed, along with project implementation criteria (e.g., cost and time of construction, job creation, etc.) is proposed. Three flood mitigation alternatives (FMAs) were evaluated: (1) no action, (2) flood warning system (FWS) and, (3) construction of levees. The methodology was applied to the case study of the Upper Watershed of the Rio Grande of Loíza in Puerto Rico. In this study, the criteria were integrated using the Analytic Hierarchy Process (AHP) and the final results produced a benefit/cost ratio for each alternative. The construction of levees was the best alternative with a ratio of 1.13 compared to 0.87 for the FWS. The framework presented takes into consideration construction criteria as well as environmental and societal impacts in order to provide decision makers with a holistic approach for studying the impacts of implementing new FMAs.

The limited available budget along with old aging infrastructure in nation magnifies the role of strategic decision making for maintenance of highway infrastructure. The challenging objective is to maintain the highway assets in a state of good repair and to improve their efficiency and performance while protecting and enhancing the natural environment. Decision makers are in need of a decision support system to consider these multiple objectives to effectively allocate funding and achieve the construction and maintenance objectives. The proposed model aims at finding optimal techniques for maintenance of multiple roadway asset items while taking into account maintenance time, cost and environmental impacts. This model creates a multi-objective problem based on the multi colony ant colony optimization. The objectives of the problem are: (1) Minimizing maintenance costs, (2) Minimizing maintenance time, and (3) Minimizing environmental impacts. The proposed approach will simultaneously optimize three conflicting objectives. The results of implementation of the proposed model on a case study are presented and discussed for a sub-set of asset items. The proposed model is validated and results reveal its robustness in generation of optimal solutions. Application of the proposed approach is implemented on the roadway asset items but it is not limited to roadways and is applicable to other infrastructure assets.

Asset management (AM) is currently a key focus area for many transportation agencies. AM helps transportation departments improve their maintenance process, have better budget allocation strategies, and extend the useful life of their infrastructure at a lower cost. All state DOTs are looking for innovative ways to manage their infrastructure assets to obtain the best return for each dollar spent. The NCDOT collects roadway condition data (through sampling) from a random set of 0.1 mile segments of the road network to evaluate the condition of different asset features against certain thresholds to estimate the need for maintenance and to determine how to allocate maintenance funding. NCDOT’s maintenance management system (MMS) is used to determine expenditures to maintain assets, assess asset condition rating improvement due to each expenditure, forecast future condition ratings, and determine optimal funding and spending plans for each maintenance activity. The objective of this paper is to present a comprehensive framework for assessing road asset condition and allocating funding for asset condition maintenance. The framework seeks to maximize condition rating while minimizing cost based on NCDOT budget constraints.

The importance of infrastructure poses significant complexity and challenges to the society under dynamic and uncertain conditions. Current risk assessment methodologies fail to address effectively the issue related to the criticality of risk in a specific infrastructure system. This issue has highlighted the need to understand both the magnitude and impact propagation mechanism of risk. This study develops a novel risk analysis method to analyze the risk criticality among various risks within specific infrastructure in the dynamic manner of stakeholder perceptions towards risk magnitude and impact propagation mechanism. The developed method is the combination of failure mode effect and criticality analysis, social network analysis and fuzzy-set theory in a group decision-making environment to facilitate the building of risk evaluation. The developed method validated in a hypothetical case of urban water supply infrastructure context. The proposed method identifies and measures critical risk at different level of value when comparing a number of hazard events. The risk analysis model developed prioritizes hazard events by considering risk decision factors and impact network analysis to address the risk magnitude and impact propagation mechanism in the context of risk analysis and decision making process. Compared to conventional risk assessment method, the proposed method provides more reliable estimation accuracy, increases decision quality and efficiency in risk management decision making. The empirical demonstration will add a significant insight and new perspective of risk analysis in order to prioritize the critical type of risk.

Sea-level rise is one of the most concerning and costly effects of climate change. Resulting sea-level rise impacts may include failure or destruction of infrastructure, immobilization due to transportation system breakdown, and catastrophic saltwater contamination of water supplies. The problem of sea-level rise adaptation is characterized by deep uncertainty that makes it complex to evaluate the economic value of adaptation investments. The key element to evaluate the effectiveness of adaptation strategies is to quantify the long-term cost of physical networks under uncertain sea-level rise scenarios. In this paper, a simulation framework is created and tested to investigate the long-term impacts of sea-level rise on infrastructure systems in order to evaluate the effectiveness of various adaptation strategies. To this end, the transformation of infrastructure systems under various scenarios of sea-level rise and adaptation strategies is modeled using the proposed simulation framework. Then, the impacts of sea-level rise are determined in terms of the life cycle costs of infrastructure networks. These estimated costs are used for evaluating the feasibility of various adaptation strategies under future uncertain sea-level rise scenarios. The application of the proposed simulation framework is shown in a case study of a road network using the sea-level rise scenarios in Southeast Florida. The results of the analysis are threefold: (1) prioritization of infrastructure assets for adaptation investment; (2) identification of the right timing of adaptation investments for different links in an infrastructure network; and (3) evaluation of the present value of adaptation investments for the entire network. The results enable more informed decision-making in order to implement robust adaptation under uncertain sea-level rise scenarios.

Due to the virtue of infrastructure projects being socio-centric, the impact of these projects on the community is highly significant. While the stakeholders’ consultation and communication at the planning phase is relatively more stringent among the developed economies, the rapid urbanization and unprecedented pace of infrastructure development in developing economies often leads to a compromise. Unlike the traditional measure of success being confined to cost, time and quality performance alone, the success of the capital intensive infrastructure projects requires a wider focus encompassing the needs, requirements and long term value creation in social context. Such a holistic assessment of the project is only possible when the representation of the respective communities is well integrated within the project development process. Focusing on a current metro rail project in an Indian city, this research develops a framework for integrating the extended community viewpoints in relation to the coordination and management practices being adopted by the concerned authorities in the construction phase of the project. Utilizing a questionnaire survey for data collection and social network theory as research method, coordination of 30 extended stakeholders have been evaluated across 23 key issues associated with the construction phase of the project. The unidirectional dataset with 45 valid responses has been analyzed using NetMiner 4.0 software for studying the communication exchanges and degree of community satisfaction within the respective social network models. Highlighting the efficiencies and inefficiencies of communication among the stakeholders, the findings contribute significantly for effective management of stakeholders in infrastructure projects and deriving success in a societal context.

Following a disaster, communities, governments, and organizations are required to make rapid decisions that will govern the path towards long-term recovery. Hazard-resistant shelter designs have long been heralded as necessary for facilitating resilient and sustainable reconstruction; however, there is sparse documentation of designs implemented. We examine the case of design and building material selection for 20 shelter projects following Typhoon Yolanda in the Philippines, using photo documentation, interview data and field observations as a means to document rates of design adoption and choices in material selection. Findings use the shelter cluster ‘8 Key Messages’ as a framework to assess level of improved shelter design. Results highlight improved foundations, roofing, building shape and site selection and identify deficits in structural elements, including connections, bracing, and joints. Findings quantify design features that saw poor uptake by organizations and hold potential to inform future practice that encourages hazard-resistant design in the Philippines and other future international disaster responses.

The increasing rate and magnitude of natural disasters increase the financial burden on host communities. This paper develops an ex ante disaster insurance profile. To this effect, and utilizing an evolutionary game theory, this paper presents three stakeholders: (1) home owners purchasing disaster insurance policies; (2) insurers offering different insurance policies; and (3) state government offering different ex post disaster recovery compensation plans. The utilization of evolutionary game theory approach allows for maximizing the host community’s total welfare by decreasing their losses depending on their needs and objective functions. The authors created a hypothetical sample of 1,000 home owners accounting for heterogeneous income levels, three insurers each offering three unique insurance plans and two different ex-post governmental disaster compensation plans. The results of the proposed model indicated that: (1) home owners evolved toward insurance plans with the least premium values; (2) insurance plans with the most comprehensive coverage did not survive through the evolutionary process; and (3) the evolutionary stable strategy is an oscillating line of chosen plans and insurers as a result of the stochastic and dynamics nature of the hazardous event. Currently, the authors are integrating the proposed model into a holistic decision making tool incorporating social, environmental, and economic vulnerability indicators. This should allow for a better prediction for the host communities’ disaster response, welfare, and resilience.

Hurricanes can significantly influence urban populations. The understanding of human movements in urban areas plays a key role in improving our disaster response, evacuation, and relief plans. However, there is a lack of research on human mobility perturbation under the influence of hurricanes. Furthermore, limited simulation studies have had access to empirical human travel data in urban areas during natural disasters. In this paper, we develop a computational model to simulate human mobility during the approaching and strike of hurricanes. Inspired by animal movements in a fragmented habitat, we examined human movements in New York City and its adjacent areas during the striking of Hurricane Sandy. We found that while the urban travels within New York City did not experience strong perturbation, the movements crossing the boundaries of the city decreased dramatically during Hurricane Sandy. Based on the patterns observed, we proposed a data-driven agent-based GIS model to simulate human movements during hurricanes. The model simulates human mobility patterns in three different scenarios: (1) the imposition of evacuation zones; (2) the occurrence of flooding; (3) and the abrupt failure of certain infrastructures. The research effort aims to inform policy-makers and support decision-making under different emergency situations that can arise during hurricanes.

Water sector infrastructures are interdependent, affecting the performance, and operations of each infrastructure. Demands placed on, and revenues generated for these systems are impacted by human interactions, including price elasticity and consumption. Population decline in shrinking cities has resulted in reduced numbers of customers, leading to underfunded and underutilized systems. This paper develops a hybrid agent based and system dynamics model for quantifying coupled human and water sector infrastructure interdependencies in shrinking cities to explore: water demand, wastewater produced, water utility revenues, and wastewater utility revenues. Data is used from two shrinking cities, published literature, publicly available data, and a survey deployed to residents of 21 shrinking cities to assess water sector interdependencies for small and medium size classifications of cities and cities operating on combined or separate sewer systems, using two Midwestern shrinking cities. This study’s significance includes quantifying the endogenous, physical water sector interdependencies, and the exogenous, complex human-infrastructure interactions. The epistemic uncertainty associated with human-infrastructure interactions is explored by incorporating stochastic parameters, enabling the estimation of the range of possible outcomes. Furthermore, the model enables the assessment of interdependencies with parameters tailored to a city’s unique characteristics, such as population decline rate, service prices, and precipitation patterns.

All infrastructure management agencies face a big challenge that every year they need to inspect the conditions for numerous assets. With routinely acquired and publicly available geospatial data and inferential geospatial modeling (IGM), there is a potential for a new approach to substantially reduce the number of survey sites required to characterize overall infrastructure condition. Using roadway pavement assets in the State of New Mexico as an example, this research investigated if overall conditions could be estimated based on inferential geospatial modeling. Three types of geospatial data were collected and used, including traffic factors, environmental factors, and topographic factors. A total of 17 variables were extracted from those data and used as explanatory. Results demonstrate that overall pavement surface condition can be effectively estimated based on traffic volumes, environmental conditions, and topography. For New Mexico, the minimum number of survey sites required to characterize overall pavement surface conditions within the range of errors expected due to human interpretation error is 4,000, which is substantially less than the ~12,000 annual samples currently collected. These results indicate great promise in the use of IGM to estimate overall infrastructure condition and an opportunity for substantial time and cost savings.

The primary aim of this study is to determine the relative effectiveness, strengths, and weaknesses of hazard mitigation plans (HMPs) in New York State. In addition to evaluating HMPs based on certain principles, they were also evaluated using real life scenarios. Further, this study determined their impact on reducing flood damage from storms, and concluded with recommendations to the state for improving the procedures and planning process of HMPs. The counties that were evaluated were considered federal disaster areas under the community development block grant action plan. Eight evaluation principles were selected based on content analysis and coding drawn from federal emergency management agency guidance documents and other hazard mitigation literature: (1) plan basics; (2) participation; (3) inter-organizational coordination; (4) hazard identification; (5) capability assessment; (6) goals; (7) proposed actions; and (8) monitoring. A combination of binary or ordinal scale were used to measure the eight plan quality principles. Results show that the principles that received the lowest scores were proposed action, monitoring and implementation and capability assessment. Additionally, results show that there is no coordination between the damages that occurred during hurricane sandy, tropical storm Lee and hurricane Irene in each county and their HMP overall resiliency score.

Resilience engineering is an emerging discipline to manage risks. Risks can be largely categorized as ‘known,’ ‘known unknown,’ and ‘unknown unknown.’ The current paper illustrates the application of resilience engineering approach towards ‘know unknown’ risks such as natural disaster whose occurrence is predictable and thus known. The authors present a model of resilience engineering applied among a community of coastal inhabitants of Bangladesh, a country located in south-east Asia. To increase the resilience of the said at risk community due to cyclone, participatory planning approach was adopted. The participatory planning method is a mechanism where beneficiaries become involved in the planning and implementation of the interventions that affect them. The intent of the participatory planning approach is to empower the beneficiaries so that they develop the resilience to manage risks during the likelihood of natural disaster as long as they receive support from organizations with predictive capabilities.

Information infrastructures facilitate communication between community stakeholders through a combination of technological and social platforms, and are fundamental to post-disaster recovery. Although post-disaster community resilience is often framed in terms of physical and social infrastructures, information infrastructures serve as a crucial link between stakeholders and critical information needed following a disaster event. During a study of the September, 2013 Boulder, Colorado flooding event, local stakeholders revealed that breakdowns in information infrastructures, known as information deficits, often hindered their perceptions of community disaster resilience. To identify these information deficits, the research team interviewed 14 local government officials, homeowners, and volunteers involved with initial flood response or long-term recovery efforts. Five types of information deficits were differentiated based on these interviews: stakeholder coordination, data management, social disengagement, technical failures, and others. These results have implications for researchers and practitioners in the field of disaster response and recovery, as it identifies post-disaster information deficits that are root causes of vulnerability within information infrastructures.

This study presents a novel multi-agent system (MAS) framework for conceptualizing, modeling, and analyzing interdependent critical infrastructure (ICI) as complex adaptive systems (CAS) to enable resilience analysis. Unlike conventional approaches that consider interdependent infrastructure as networks of physical assets, the MAS framework conceptualizes interdependent infrastructure as interacting networks of social and physical entities and processes at different levels of hierarchical organization. The interaction between physical and social entities is captured at different levels of interaction proposed in System-of-System taxonomy and modified for modeling critical infrastructure systems and social agents. The proposed MAS framework is capable of abstraction, realization, and implementation of interdependent critical infrastructure systems as complex adaptive systems. The implementation of the proposed MAS framework is conducted through a nested high-level Petri-net method capable of: (1) abstraction of dynamic behaviors and interactions between social actors and physical networks, and (2) multi-level aggregation of dependencies in modeling critical infrastructure systems. The contributions of the proposed framework are threefold: (1) abstraction of dynamic behaviors and interactions between social and technical entities; (2) multi-level aggregation and implementation of interdependent critical infrastructure (ICI) networks as complex and layered systems; and (3) formalized modeling and simulation of infrastructure’s multi-agent systems (MAS) using high-level petri net.

Municipality Engineers and decision makers have to consult and utilize large data sets to make informed management decisions concerning individual and/or integrated infrastructure assets. Such data is gathered and processed more than once, by different users; furthermore, it is usually stored in different repositories and in different formats. This reality necessitates the design and utilization of databases that are specially structured to facilitate the management of such massive volumes of interrelated data. This paper presents a multi-tiered database schema for integrated infrastructure management. The schema is multi-tiered to be usable by different size municipalities; adopting different approaches for asset management. The presented schema comprises a basic tier for managing basic data of separate assets through running common asset management processes, a second tier for running advanced asset management models, and a final tier for managing special data required for specific usage. The database schema can be utilized for managing basic data of separate assets, and its full scale implementation allows managing integrated assets using data generated by different up-to-date asset management tools. The database schema is implemented in ArcGIS and applied to a municipal database containing data for water, sewer and road networks to demonstrate its applicability and essential features.

According to the Environmental Protection Agency (US EPA), one in four households in the United States rely on decentralized/onsite wastewater treatment systems. Unfortunately, no federal monitoring standards or performance criteria exist for decentralized systems. Local governments are supposed to regulate these systems from permitting through operation and replacement. However, agencies rarely have the resources to regulate beyond installation. To address this issue, the US EPA released five management models in 2003 that were adopted and adapted in a variety of fashions across the nation. However, these models have not been updated and lack technological recommendations. In an effort to merge technology and current management models, this paper (1) analyzes the US EPA recommended management models in order to understand where responsibilities lie for the decentralized wastewater management and (2) demonstrates which parties the recommended practices most heavily rely on and how remote monitoring (RM) can alleviate those responsibilities. Remote monitoring (RM) is not a new technology and has been widely applied to centralized wastewater treatment systems, but has seen little application in decentralized systems.

Rehabilitation of aging roads has a significant impact on pavement performance and hence vehicle fuel consumption in transportation networks. Therefore, thorough analysis and evaluation of highway rehabilitation decisions and strategies can result in significant savings in fuel consumption in transportation networks. However, it is a challenging task to optimize highway rehabilitation decisions in order to minimize network fuel consumption and maximize net public benefits while satisfying several constraints such as limited funding and targets established for overall pavement performance. This paper presents a new model developed for optimizing and planning highway rehabilitation efforts that is designed to: (1) identify candidate rehabilitation treatment alternatives to deteriorating pavement; (2) evaluate and forecast the impact of implementing such treatments on pavement performance; (3) estimate total network fuel consumption as a result of selected rehabilitation decisions; (4) evaluate the cost of travel delay due to construction operations; (5) measure the impact of rehabilitation decision making on the expected savings in road user costs; and (6) optimize rehabilitation decisions to identify highway program(s) that simultaneously minimize network fuel consumption while maximizing net public benefits. The model is applied to a hypothetical example of an aging transportation network example in South Florida and was capable of generating optimal tradeoffs between public benefits and fuel consumption. This model should prove useful to state highway agencies in identifying and implementing cost-effective and energy-efficient highway rehabilitation programs.

The drinking water infrastructure system in the U.S. consists of approximately 880,000 mi of pipelines. The ASCE 2013 report card estimated that over 240,000 water main breaks occur each year in this network of pipelines. A need exists to optimize and prioritize decisions in response to the failure of water pipes in distribution system. The objective of this paper is to develop and demonstrate a methodology that uses econometric analysis to estimate the number of water main breaks in a water distribution system. Econometric modeling provides a significant advantage in predicting the frequency of water main breaks based on historic data. The primary contribution to the body of knowledge from this research study is a novel methodology approach to estimate the number of water main breaks and the synthesis of significant parameters impacting water main breaks in a water distribution system. The water main breaks data for a 21-year period from the City of Indianapolis were used for modeling approach.

This paper presents an intensive survey of literature, focused on the different aspects of infrastructure evaluation systems as related to the infrastructure sustainability and resilience. The literature dictates the exploration of the underlying connections of the three main themes of “greenness”, “resilience” and “sustainability” of infrastructure systems and how they could evolve into one comprehensive infrastructure evaluation system (CIES). After reviewing most of the widely used infrastructure evaluation systems, certain deficiencies appeared across the board that should be addressed in a CIES. The paper concluded with a recommendation of a preliminary main framework for a comprehensive evaluation system development.

Bridge pier scour is the leading cause of bridge collapse in the United States. The risk of scour related failures increases in the wake of severe storms, such as those experienced in recent years. Enhancing the resiliency of the transportation network and its ability to withstand future major weather events is largely dependent on the efficacy of bridge scour mitigation programs. However, systematic treatment of bridge piers for scour mitigation is often beyond the scope of available state budgets. It is therefore highly desirable to develop algorithms to prioritize bridges requiring scour mitigation in a given region. In this study, a case study is presented for risk assessment conducted in the state of New York to determine the critical bridges in most need of initial mitigation program funding. A risk assessment tool was implemented to evaluate the risk associated with each bridge, and that of the community that uses the bridge. Risk was calculated based on hazard level, exposure and vulnerability levels. The risk assessment model was found to effectively prioritize bridge scour mitigation in the state.

Electric power supply grids are vital to social and economic activities as well as to public safety and wellbeing and are ranked as the highest critical infrastructure. There are substantial adverse impacts on society when power grids fail such as disruption to traffic and shut down in the operation of other critical infrastructure elements. This paper presents a novel method to assist in forecasting the probability of power outage based on weather condition in four Canadian provinces—Quebec, Ontario, New Brunswick, and Nova Scotia. System disturbances reports, provided by the North American Electric Reliability Corporation (NERC) from 1992 to 2009, have been scrutinized to determine the conditions that lead to power outage. Based on the reports above, weather condition is found to be a major cause behind power outage that justifies the necessity of a comprehensive study in this area. As a result, a forecasting model for power failure based on weather conditions is developed by artificial neural network (ANN). Once the prototype model is trained, it is able to predict the probability of power outage occurrences by utilizing forecasted weather data for a specific location. Finally, a case study is presented to illustrate the applicability and accuracy of the developed method.

State transportation agencies (STAs) are using a variety of accelerated construction techniques to shorten the duration of highway construction project. While accelerated highway construction projects may impact communities in terms of traffic congestion, safety issues and other public inconveniences, they also can provide opportunities to highlight innovative accomplishments and increase trust between STAs and the traveling public. As some studies have shown, effective management of outreach strategies is a key success factor of accelerated construction projects. One of the main goals of this study is to gauge the effectiveness of public outreach tools and strategies in meeting different communication goals, including traffic avoidance, increasing driving awareness, decreasing impatient behavior, and building trust with the public. The data for this research is based on a survey of 71 experts from 40 different STAs. ANOVA analysis indicates that the effectiveness of outreach tools vary significantly in achieving different communication goals during the construction phase of accelerated highway projects. In addition, the results allowed us to find the most appropriate communication strategies, based on project types and target audience. The findings of this research can help STAs to better manage their outreach activities during the accelerated construction projects.

Transportation infrastructure asset management is a data-driven process. Great efforts have been made by state highway agencies (SHAs) in asset management in recent years. To investigate the challenges that SHAs are still facing in current practice, a web-based survey was distributed across different functional divisions of SHAs through AASHTO’s Standing Committee on Highways. SHAs responded to questions about their current practice in transportation infrastructure asset data collecting, managing and sharing. The key implementation hurdles were summarized, which indicated that in current practice, the construction documentation process and the asset in-place data collection process in SHAs are two separate entities, and that this discrepancy caused data loss and duplicate data collection. Further, the survey included questions aimed at seeking the expectations from SHAs towards future improvement. To overcome the data blockage in the current practice, the paper-based data is expected to be replaced with electronic data, and a mechanism is needed to facilitate the flow of data items collected in construction documentation to asset management information systems from the long-term operation and maintenance (O&M) perspective. The results and findings are valuable in emphasizing the current practice hurdles in collecting, managing and sharing transportation infrastructure assets, as well as assisting SHAs to make efficient decisions for future research and implementation.

Our built environment and communities are facing an increasing rate and magnitude of natural disaster. Accordingly, the different stakeholders are concerned about their investments’ vulnerability. This requires a new generation of decision support tools that integrate the host community’s vulnerability assessment while taking into account the stakeholders’ interactions, needs, and preferences. This paper develops a prototype agent based model that utilizes an environmental vulnerability indicator to better guide the decision making process of the interrelated stakeholders. The four step researched methodology incorporates: (1) implementing an environmental vulnerability assessment tool; (2) developing the different stakeholders’ objective functions, potential strategies, and learning behaviors; (3) creating an interdependent agent based model that simulates the associated stakeholders and the vulnerability indicator; and (4) interpreting and analyzing the results generated from the developed model. The proposed prototype model was developed and tested on the post-Katrina development in three coastal Mississippi counties. The model optimized the different stakeholders’ decision actions that increased their individual utility functions and decreased the host community vulnerability. This research approach should collectively maximizes the host community welfare and decreasing its environmental vulnerability, thus, achieving a sustainable disaster recovery.

Considering the severe consequences of building fires, evacuation planning is regarded as one of the most important issues in the design and management of facilities. During fires, occupants face significant uncertainty as to the severity of the situation and must continuously interpret new information from various sources (e.g., other occupants and environmental cues). Based on this influx of new information, and in conjunction with the individual’s coping capability under duress, the occupant’s perceived risk of the fire situation can change during the course of his/her evacuation. Despite the severity of such situations, the role of dynamic risk perception has been overlooked in the literature. Therefore, this research aims to analyze how dynamic risk perception influences evacuation performance through simulation modeling. An agent-based building fire evacuation simulation model and three different scenarios are examined to investigate the following evacuation performance metrics: evacuation rate, response time, and movement time. Our findings showed that after a fire alarm sounded individual perceived risk increased, resulting from observing the behavior of others, thereby initiating expedited evacuation movement. Similarly, increased perceived risk resulting from detecting fire smoke significantly accelerated occupant movement towards the exits. These findings highlight the significant role of dynamic perceived risk on fire evacuation performance. Understanding the importance of perceived risk on evacuation outcomes, facility managers could attempt to manipulate additional information that affects occupant perceived risk during the different stages of fire evacuations to improve evacuation effectiveness and efficiency.

Properties located adjacent to the wildland urban interfaces (WUIs) are vulnerable to wildfires. The danger of high severity wildfires is growing due to the increase in temperature and drought. In addition, as more housing projects are progressing towards forested lands, the wildfire-vulnerable built environment is increasing. The exposure and sensitivity of the aforementioned properties to wildfire damage depends on structural and land characteristics, respectively. Vegetation type and landscape determine wildfire propagation scheme and affect the duration and extent of the wildfire danger to the structure. On the other hand, the property’s structural materials, roofing system, deck, and external wall system, among others, contribute to structural ignitability or sensitivity to wildfire. In this study, these heterogeneous attributes are considered in a multi-criteria vulnerability assessment. This paper attempts to provide an automated approach for assessment of vulnerability using cadastral and GIS-based data. Risk assessment guideline for homeowners (RAGH) and international wildland urban interface code of 2015 (IWUIC-2015) are applied and compared for this purpose. While IWUIC-2015 is stricter in rating of the criteria and sub-criteria, it is more permissive in the classification of the final vulnerability score, changing the location and arrangement of high vulnerability clusters from that resulted from RAGH model.

Bridges are a key component of transportation infrastructure. Currently, decisions on what material should be used to build a bridge superstructure are primarily driven by the initial costs in design/construction, but subsequent repair and maintenance costs are less emphasized. Moreover, limited budgets for bridge construction and maintenance require new processes to optimize decision making in choosing appropriate materials for bridges. This study aims to characterize the performance of steel bridge superstructures considering factors such as age, average daily traffic (ADT), design load, and structure length. This paper applies data mining techniques to the 2013 national bridge inventory (NBI) database for bridges whose superstructure material is steel or steel continuous and whose deck type is concrete cast-in-place. This study develops a model to predict the probability of a steel bridge superstructure being deficient given the characteristics of the bridge. The contribution of the paper is that the analyses will help stakeholders better understand what parameters are significant to the superstructure deterioration, and what type of steel bridges are more likely to have deficient superstructures.

Formwork system selection is crucial for a project’s success. Despite its importance, contractors select formwork based on individual experience without using a consistent decision-making method. Several numerical methods for formwork selection have been proposed to fill this gap. Nevertheless, the context in which the numerical methods are developed limits their scope of applicability. In this paper, we review previous decision-making methods used for formwork selection and evaluate their applicability in the context of an affordable housing project in Ecuador. The unsuitability of such previous decision-making methods for this case study led to the implementation of the choosing by advantages decision-making method (CBA). We provide justification for the use of CBA, describe its implementation process, and analyze information gathered from interviews with the participants involved in this experience. Findings reveal that CBA helped decision-makers represent the context of their case, leading them to select the alternative that best suited the characteristics of their project. Interviews with project participants indicated that the discussion sessions of CBA generated a social process in which debate, argumentation, and rhetoric played an important role in the final resolution.

Those involved in projects on which lean practices are present indicate that improving production through the application of lean naturally leads to enhanced worker safety. Accidents and worker injuries result in added time to clean up after the incident, repair damaged work, and attend to the injured worker, all of which represents waste. Previous studies have identified that some lean practices positively impact safety management practices; however, there is still little understanding of the direct relationships between lean construction practices and the risk of worker injuries and fatalities on construction sites. To address this gap in knowledge, this paper builds on previous research on lean and on safety and presents a study carried out through case studies to investigate the impacts of lean practices in hazard mitigation and safety performance. By doing so, the research intends to provide evidence for the need to integrate both disciplines in order to enhance construction site safety while improving the efficiency of delivering projects. The study revealed that in some cases practices that change the way people work could have helped improve the project’s safety performance. Nevertheless, collaboration between lean practitioners and safety professionals is needed to implement more reliable safety controls.

Process-plant projects are inherently complex and successful delivery requires effective coordination of multi-disciplinary teams across project phases. While each major project phase—engineering, procurement & construction will have a preferred sequence of deliverables, this sequence will be disrupted due to constrains and requirements of other phases. Field studies on ongoing projects showed that there were significant delays in construction of the piping package due to delays in design and construction of structural and civil packages. Further analysis of the delays revealed that the engineering for structural and civil components were not sequenced appropriately due to lack of information on construction sequence and priority. The availability of information on the detailed construction sequence will enable the engineering/procurement teams to synchronize their work based on these requirements. Aligning the sequence of upstream activities to meet the needs of downstream requirements is a result of pull planning and is a basic concept in lean thinking. One of the challenges in implementing pull planning for the current situation was that appropriate sequencing information had to flow from the construction to engineering and procurement. Further, this information had to be structured to ensure that the network of appropriate upstream stakeholders is appraised on the specific downstream requirements. In order to structure the information, a schema for mapping spatial and information dependency between the various disciplines was developed. The schema was implemented using a relational database platform along with querying features and a user-interface. The system was tested using data from an on-going project and it was found that the results supported the implementation of pull-planning and enabled better coordination of upstream activities to support construction requirements.

Tracking work-in-progress on construction sites requires establishing two-way flow of information between project managers and field personnel so that changes and performance issues with respect to task assignments, scope, status, and completion can be communicated effectively. Recent research has focused on minimizing decision bottlenecks and lowering coordination costs caused by inefficiencies in information sharing between project participants via leveraging 4D BIM and mobile computing platforms. Nevertheless, effective communication of who is doing what work in what location and comparing actual and planned progress in a timely fashion still requires preparing/updating 4D BIM with details that can reflect daily tasks, breaking the model into various locations, and then capturing task information onsite and manually updating them to 4D BIM. To address these inefficiencies, this paper presents a web-based work tracking system that builds on 4D visual production models, generated via superimposing image-based 3D point clouds with 4D BIM. These models allow project participants to directly commit to work tasks without the need for addressing difference in the level of detail between 4D BIM and actual work in progress, communicate performance deviations based on commitments from coordination sessions, measure readiness of the upcoming tasks based on inter-dependencies of current schedule tasks and their constraints, and highlight locations that are at-risk of potential performance problems. The benefits of the system in providing enhance awareness of ongoing work and better flow of information are discussed based on experiments conducted on a large-scale construction project.

Three-dimensional (3D) paths of resources have been proposed in construction management, as an efficient way for measuring labor productivity. These paths are either extracted by using sensors such as global positioning system (GPS), radio frequency identification (RFID), and ultra-wideband (UWB), or based on cameras placed at jobsites for surveillance purposes. However, the tag-based methods are seriously limited by privacy conflicts since they are not welcome from the personnel. On the other hand, the computer vision based methods have not achieved full automation in measuring labour productivity because they require prior knowledge of the type of tasks performed in specific working zones. This is associated with the lack of depth information. For this purpose, this paper proposes a computationally efficient computer vision method for matching construction workers across different frames. Entity matching is a process that corresponds to a compulsory step prior to the calculation of the 3D position. The proposed matching method, is based on epipolar geometry, template and motion similarity features. The main result of this process is to provide a method for the acquisition of the 3D paths that compose the detailed profile of a construction activity in terms of both time and space.

Prefabrication is believed to improve construction labor productivity by increasing direct work rates and reducing rework and onsite injuries. Anecdotal evidence shows that electrical contractors are able to improve their average labor productivity by 30% by bringing 20–30% of their field labor hours into the prefabrication shop; however, this statement may not convince some electrical contractors. These contractors may not consider adopting prefabrication since they are more comfortable with conventional methods. The extra effort spent on planning and constructability coordination with other trades may dampen their motivation to pursue prefabrication on their projects. This paper presents a case study of an electrical contractor who recently adopted prefabrication for an office building project. Data were collected to quantify the probable benefits of prefabrication in building electrical construction. The actual man hours spent on the project—as a result of using prefabrication—were compared with the original estimation based on conventional electrical construction methods. In addition, the researchers performed work sampling on electrical workers; the results were compared with conventional methods. The results showed 9% productivity improvement and 16.7% savings in work-hour as a result of using prefabrication.

The purpose of this study is to investigate the feasibility of a two-prong strategy for estimating optimal labor productivity in a labor-intensive operation where workers perform sequential and parallel tasks. The first prong represents a top-down approach that estimates optimal productivity by introducing system inefficiencies into the productivity frontier. A qualitative factor model (QFM) is used to determine the impact of those inefficiencies. This top-down approach yields the upper level estimation of optimal productivity. The second prong is a bottom-up approach that determines optimal productivity by removing non-contributory work from actual productivity in a discrete event simulation (DES). This bottom-up approach generates the lower level estimation of optimal productivity. An average of the upper and lower limits reveals the best estimate for optimal productivity. Optimal productivity is useful as an absolute benchmark for gauging efficiency and is a radically different approach from traditional methods of comparing actual against historical productivity, which only provides relative measures of efficiency. A case study was conducted at a sheet metal duct fabrication activity. The activity included eight tasks and forty-two actions. Using QFM, the loss in productivity due to system inefficiencies was found to be 0.39 ducts per crew-hour. Using DES, the loss in productivity due to operational inefficiencies was found to be 0.50 ducts per crew-hour. The productivity frontier computed for the activity was 2.83 ducts per crew-hour. Finally, the estimate of optimal productivity was determined to be 2.07 ducts per crew-hour. Given that actual productivity in the field was 1.20 ducts per crew-hour, the work was only 58% efficient as compared to optimal productivity. This study contributes to the body of knowledge by providing a methodology to estimate optimal productivity in a labor-intensive operation.

The application of portfolio management is increasing in large organizations as they are more inclined to manage several small construction projects instead of a few mega-projects. A detailed survey launched to the members of the Construction Industry Institute and Construction Management Association of America showed that the majority of owner organizations do not have systematic approaches to form their portfolios. They usually rank candidate projects using one criterion such as net present value or internal rate of return and then select projects based on their ranks considering the budget constraints. Given the complex nature of businesses, many other criteria (geography, politics, strategic objective, competitors, construction related risks, etc.) can play a role in determining the importance of a project to the organization’s long-term plans. In this research, a framework is designed to help the owners of large construction programs form balanced project portfolios in terms of business lines, project types, and construction risks. Several criteria in different areas of finance, economy, and safety for ranking projects are identified using a comprehensive literature review and a detailed industry survey. A multicriteria decision-making method, PROMETHEE, is identified as a suitable method for ranking projects due to its flexibility in normalizing the differences between the criteria of competing alternatives and providing consistent results. This framework is illustrated through a case study.

The objective of this paper is to define a method to measure construction crew motivation assessed at both the individual and crew levels. After performing a literature review to identify relevant factors, we present a methodology for assessing overall crew motivation based on the aggregation of expert opinions. The method entails asking experts (e.g., crew workers or supervisors) to provide inputs for the various measures, and then using a fuzzy aggregation approach to combine individual and crew-level responses. The findings of this paper will help to model and analyze construction crew behavior, which will contribute to better prediction and management of construction labor productivity and project performance.

The Indian construction sector contributes about 10% to nation’s GDP and employs approximately 35 million people. The construction projects in India are facing key challenges such as time and cost overruns, low-productivity and lack of skilled labor. The major portion of the industry’s workforce consists of transient or migrant workers. Studies in other countries have shown that absenteeism and turnover of workers leads to increased labor costs and decreased productivity. The socio-economic situation in India is very different from the countries where studies were reported earlier. The present study aims to address the issue of absenteeism and turnover of migrant workers in the Indian construction industry. Interviews with the project managers and labor sub-contractors were conducted to ascertain the causes for high absenteeism and turnover of workers in 15 construction projects in India. The projects were geographically distributed from six different states of India including Delhi, Gujarat, Karnataka, Maharashtra, Pondicherry, and Tamil Nadu. Findings of the study indicate that illness, injury, lack of basic facilities, and payment delays are the significant factors contributing to absenteeism and turnover of workers. Strategies and best practices recommended from this study will help project managers and field managers to better manage migrant workers, and improve project performance.

Falls are still a leading cause of fatalities and serious injuries in construction. Although extensive research has attempted to identify factors affecting the risk of falls, limited studies have investigated the relationships among these factors to better prevent fall accidents. This study proposes a Bayesian Network (BN) approach to assess the safety risk of fall from heights. The proposed approach consists of a conceptual and generic-model structure, processes for customizing the generic model, and a computational BN module. The generic-model structure was developed based on an extensive review and evaluation of causal factors leading to falls. The computational module was developed based on Bayes’ rule for inference to customize model input and job site characteristics. The approach provides probabilities associated with different states of safety risk and allows practitioners to assess preventive actions and safety strategies to reduce risk of fall. The BN-based approach was successfully applied to a construction operation in a high-rise building project. The proposed approach offers a quantitative assessment tool for the safety risk of falls from heights.

Construction project teams are increasingly operating in dispersed arrangements in which communications predominantly take place via information communication technology (ICT) with a dose of face-to-face interactions. However, a review of literature reflects a scarcity of research on how shifting to dispersed teams affects the perceived quality of communications in construction project teams. The present study attempts to address such a gap in the body of knowledge. To this end, 12 major indicators of quality of communications were identified through a review of literature. This was followed by conducting 17 semi-structured interviews with experts in the industry to customize the indicators for the construction context. Based on the identified indicators, a questionnaire survey was designed for elucidating the perceptions of construction practitioners about changes in quality of communications in dispersed teams in comparison to fully collocated ones. Drawing upon the data collated from 285 duly completed questionnaires, confirmatory factor analysis (CFA) was deployed to define the major areas of change in quality of communications in dispersed teams as perceived by the respondents. The findings showed that shifting towards dispersed working results in a change in perceived quality of communications in ten areas mainly reduction in completeness and reliability of communications. The present study contributes to the field by providing a list of indicators for assessing the quality of communications for construction project teams. Additionally, the findings provide an illuminating insight into the primary changes in perceived quality of communications in disperse team arrangements.

Research has demonstrated the potential for real options (RO) to enhance project value by managing uncertainty through contractual and system’s design decisions. Despite this, RO theory is not widely used within the discipline of construction projects. Researchers have shown a gap between the view of RO and its application in actual project settings. Previous studies on the barriers to RO use in the construction industry (CI) have several limitations such as not decomposing the barriers to basic underlying factors and not explaining how to overcome the barriers. This study has identified and categorized about 20 partially independent phenomena influencing the application of RO in the CI. This study provides a linkage between studies that explain the barriers to RO use and those offering solutions to overcome these barriers. Many keywords and useful references are offered for future research on each of the barriers, and the reader can obtain guidelines for future research and find relevant papers on resolving RO barriers in the CI.

The use of Mobile IT devices on construction projects is becoming more powerful and increasingly accessible and efficient. The traditional methods for field data collection and communication, such as written record and photograph, carry a higher possibility of making mistakes and longer task completion times. Recently conducted surveys and interviews of staff at state transportation agencies (STAs) in the areas of design, construction, and asset management reflect the current and potential use of hand-held mobile IT devices is on the rise. Based on the investigation results, around 90% of STAs benefit from the application of mobile IT devices in daily contact, accessing standards and manuals, and record entry. STAs believed overall return on investment of mobile IT devices could be around 50%, although about 50% believe they are low implementers of the technologies. While, for most, implementation is in early stages and requires more investment, mobile IT devices are being adapted to overcome shortcomings of traditional on-site communication methods. This means that applications have broad prospects and potential in construction management. The primary contribution to the body of knowledge is to gain an understanding of current practices and present opportunities for STAs to advance methods and improve efficiencies in their use of mobile IT devices nationwide.

The study of complexity in construction has raised a valid question: Is construction really complex or just complicated? More importantly, how to test which one is the best characterization of a construction project delivery system? The answer to these questions, as well as that of the title, is developed in this paper through a systematic story: Comprehensive literature search is firstly used to make an embedded conceptual analysis of basic concepts of complex and complicated; behavior analysis of complex systems—as opposite to ordered (complicated) systems; metonymic mapping of complex and complicated system to construction domain, considering construction delivery as a system—product (object), organization and process (social systems), and implying complex theory on the project execution; in the end, discussion of new approaches to construction project delivery management. From the synthesis reasoning of this paper, construction project delivery systems could be considered as complex systems rather than complicated ones. Understanding complexity in construction management is important for two reasons: (1) to visualize how both complicated and complex traits exist in a construction project (object and social systems), and (2) to identify for stakeholders new types of managerial competencies and tools that reflect the understanding of complexity in construction.

Numerous researchers have investigated various aspects of building information modeling (BIM), uncovering many important issues in its development and application. However, there has been inadequate investigation of the value to be gained from the integration of BIM in the facilities management (FM) phase of a building’s lifecycle, despite its considerable potential. The purpose of this paper is to explore the value inherent in the deployment of BIM in FM. It identifies key FM operations that lend themselves to BIM, reviews the enabling technologies, and explores the potential value of BIM/FM integration to the building owner’s organization. The associated challenges are also explored. Three case studies of university FM departments and their applications of BIM within FM practice are used to illustrate the key issues and highlight important considerations. Salient aspects of BIM/FM integration and the concomitant effects on productivity, performance, workforce engagement, business processes, economic efficiency, and organizational culture, are also discussed. The paper concludes that strategic BIM deployment in FM is critical to realizing the full potential of BIM use in a building’s lifecycle. It is also considered vital in demonstrating the business value of BIM in facility operations and management.

In construction industry, “project” is the most common organization form. Regarding as temporary organizations, projects have some unique features such as temporariness and institutionalized termination, specific team structures that are set up to accomplish a particular and one-off but rather complex task, and limited duration. Such features set them apart from permanent organizations. The specific features of temporary organizations, particularly the limited time issue, may have a special impact on how individuals cooperate and support each other in finishing the task. This paper studied organizational citizenship behavior (OCB) in project context. First, a quantitative, cross-sectional study was conducted to show whether there is citizenship behavior in projects. Second, a comparison between OCB and project citizenship behavior (PCB) was made. After that, this paper derived hypotheses and studied how PCB improves project performance. Finally, implications of the findings were discussed.

The significant influences of leadership styles on team performance in construction projects have been well documented in a variety of empirical studies. Although general organizational behavior theories are often used to explain the causality between organizational factors, including leadership styles, and team performance, few efforts have been made to investigate the implications of the contextual factors of a construction project. This paper aims to analyze the effects of two common leadership styles, directive and empowering, on the performance of temporary project team with agent based modeling (ABM). The contextual factor highlighted in this study is the temporary nature of a project team. In the simulation experiments, a team comprising both permanent and temporary members is formed to execute a construction project. The portion of temporary members is adjustable in the simulation to capture different levels of temporariness. Directive and empowering leadership styles are modeled as whether the team leader tends to give direct instructions to the subordinates or to decentralize the decision making by granting authority to subordinates. The results indicate that leaders should choose suitable leadership style depending on situations. When there are comparative advantages, directive leadership style leads to a better efficiency when team is more permanent and the competence of the team leader is beyond a certain level. In contrast, empowering leadership style performs better in relatively temporary setups. Recommendations for future research are also provided.

Good collaborative relationship (CR) among the partners of IPD projects is a premise to successfully exploit the advantages of IPD to the full. However, the multi-parties collaboration of IPD projects involves multilateral relationship, which is very different from the bilateral relationship of traditional project delivery; and these stakeholders of IPD projects from various organizations have to change their mind and practical paradigm simultaneously to adapt to this innovative delivery (IPD) which has quite different business structures compared with traditional project delivery; therefore there are more challenges to maintain good CR during the implementation of IPD projects. This paper aims to explore the factors impacting CR through three focus groups including owner group, designer group and contractor group. The critical factors of CR in IPD projects were identified and classified into 4 main dimensions: interaction, stakeholders’ characters, contract and organization. Based on the results of focus groups, we propose several recommendations to better manage the CR among the partners for IPD projects.

Assignment of jobs to a construction crew is not a trivial task. The optimal job allocation should consider the characteristics of workers as well as brain resource requirements of the job. Failure in proper allocation of jobs to workers can lead to an inconsistent workload distribution among workers which can then results in a decrease in the overall productivity. The inconsistency in the workload of workers can cause imbalanced fatigue rates and skill level stagnation particularly for workers who are less involved. This paper proposes a framework for regulating job allocation to workers by accounting for workloads and characteristics of worker. Workloads are attributed to visual, auditory, cognitive, and psychomotor brain resource requirements of different tasks. To achieve a realistic assignment of jobs, the framework incorporates workers’ capabilities in terms of skill type and skill level. Workforce management strategies established by a contractor, including safety improvement, skill enhancement, and multiskilling, determine approaches toward minimizing workers’ workload variance within a crew. Application of the proposed framework is demonstrated using a real case concreting project with a simple trial modification in task assignment process under “safety improvement” strategy.

Promoting collaboration in the architectural, engineering, and construction (AEC) industry, the innovative and contractually followed integrated project delivery (IPD) method has been instituted to increase the number of information sharing interactions among team members and improve team integration. However, project outcome effectiveness depends on not only the number, but also the quality, of these information exchange interactions. This paper posits that goal alignment in IPD project teams fosters the creation of reasoned and productive information sharing connections which enhance team members’ ability to monitor key project-related information, impacting their tasks and ultimately project outcome effectiveness. To test this model, we surveyed 185 team members from 21 project teams that contractually followed IPD and analyzed data at the project team level. The model was first validated via a confirmatory factor analysis and then tested using path analysis. The results indicate that the effect of goal alignment on team members’ perception of IPD effectiveness was mediated by monitoring in IPD project teams. If team members tie their goals, they make the effort to seek and search key information to accurately monitor project progress.

Motivation is crucial to the performance and satisfaction of an individual in all facets of life. The construction industry is saturated with challenges for all individuals from the tradesperson through the owner. However, the challenges compound when they start to negatively affect the performance of projects. Recent studies have shown that there are several variables that help define how people are motivated to work. The objective of this study is to explore the drivers that motivate project managers on construction projects. To achieve this objective, a web-based survey was developed to better understand what motivates project managers in construction. Statistical analyses were performed to test the factors and their relationships. The results of this study show that participants agreed on being motivated by belief in the cause, a respectable leader and a challenging project. It also shows that age can impact some drivers of motivation, such as how much they believe the team affects personal motivation, but not other drivers including assumption of a leadership position.

Several studies recognize the existence of a relationship between learning and experience, on one hand, and productivity and quality of work, on the other hand. This paper examines, through the use of a case study, the relationship between productivity, quality, and learning within the unique labor pool of the Middle East and North Africa (MENA) region. The analysis of the case study confirms the existence of a positive correlation between task repetition (as a proxy for learning) and productivity. The data for the case study was obtained from productivity logs prepared by subcontractors and from material submittal and shop drawings approval logs prepared by the construction manager. The analysis of the case study did not show a positive relationship between learning and quality. This outcome is in opposition with the literature.

Construction organizations must often facilitate the search for knowledge so that individuals can easily identify solutions from different parts of the project. In order to identify ways of improving and facilitating the knowledge search process, we must first identify factors that are considered to be important when searching for knowledge. This research addresses this need by asking the following question: what factors affect individual knowledge searching choices? To explore these question, the research team conducted 16 interviews at a large infrastructure engineering organization. Using inductive coding techniques, the interviews were qualitatively analyzed. Results showed that individuals searched for knowledge by approaching potential contributors or intermediaries who could direct them to potential contributors. The knowledge searchers sought potential contributors who had high levels of expertise, responsiveness, reachability, and intermediaries—individuals or tools—who were highly connected, specialized in the field, and had increased search capability. These results have essential implications for practitioners as they identify factors that are important to the process of knowledge searching.

More and more researchers recently discover the potential that serious games have for supporting construction management research and education. However, little theoretical guidelines exist of how to best use games for these two purposes. Many applications of serious games in the field lack a sound theoretical background, which often hinders broad generalization and formalization of results. This paper addresses this shortcoming, by providing theoretical insights into the effects of serious games on students and practitioners alike, drawing on organization science theories. In particular, the paper suggests that sensemaking theory is a suitable theory to support researchers that want to draw conclusions about organizational behavior of practitioners that are playing serious games. The paper also suggests, that activity theory is suitable to analyze how practitioners and students can learn from playing serious games. Both theories are demonstrated using game playing activities from a past research project at the University of Twente. The paper closes with a discussion of these two theories reflecting about how different abstraction levels of games support teaching as well as research activities.

Construction work involves significant physical, mental, and temporal task demands. Excessive task demands can have negative consequences for safety, errors and production. This study investigated the magnitude and sources of task demands of a concrete crew, and examined the effect of the production practices on the workers’ task demands. The operation examined was the construction of a cast-in-place concrete building under high schedule pressures. Extensive field observations and interviews with the supervisors and crews identified the production practices. The NASA task load index was used to measure the perceived task demands of two work crews. Interviews with each crew member were used to identify the main sources of the perceived demands. The findings suggest that the production practices influenced the task demands in two ways: one set of practices mitigated the task demands—such practices were related to work organization, task design, resource management, and crew management, and a second set of practices increased the crew’s ability to cope with and adapt to high task demands—such practices were related to work planning and crew management.

It is logical to pursue cost and safety benefits by substituting practice on a training simulator for a portion of the training that traditionally occurs in the field on actual heavy construction equipment. However, more understanding is required to determine the most useful amount of simulator practice to maximize transfer of the desired skills to operation of a real machine. We report a study conducted at the John Deere and Company Demo Site in Coal Valley, IL, which compared the operator skill development of individuals who trained on an excavator simulator to that of persons who trained exclusively on a real excavator. Both groups practiced a trench-and-load task and were tested on a common real machine. Performance of the simulator group on the real machine improved across daily sessions, attaining the same level as the real-machine group after 10 hours of practice.

Expanding geographically, adding trades, and competing for larger or new types of projects are examples of market entry approaches used by construction firms to grow their organizations. Growth provides the opportunity to develop and motivate personnel, expand the brand, and mitigate market risks. Unfortunately, market entry also may cause business hardship. Although the literature suggests having a standard decision process leads to better outcomes, a formal, written protocol for addressing domestic market entry decisions could not be found in the construction literature. This paper briefly outlines a method for creating an applied market entry decision framework, and then details one critical aspect of this process: prioritization of decision factors. Key decision factors are uncovered through a review of the literature and interviews with industry professionals, and then prioritized using modified Delphi workshops. The top two decision factors for both workshops were ‘Market need’ and ‘experience and abilities of the champion.’ The results contribute to the construction body of knowledge by providing the most important factors for sheet metal and HVAC contractors to consider when entering a new market.

The specific aim of this study is to identify work-life balance (WLB) perceptions in relation to how supervisor’s leadership impacts individuals’ career intentions. To do so, a survey instrument was distributed to young project engineers. The majority of the questions in the survey used a Likert-scale format designed to elicit the participant’s opinion of how well they felt their supervisors were at applying key leadership competencies tied to WLB. Other questions asked how long they planned on staying employed with their current position and in the construction industry. The preliminary results provide evidence that a relationship does exist between participants’ perceptions of how effective their supervisors are in applying the 12 Pillars of Leadership and helping them to achieve WLB. The results from this study are expected to provide valuable information on understanding how WLB is perceived by project engineers who will be taking leadership roles in our industry in the next few years. Exploring the perceptions of young professionals regarding to WLB provides new insights for engineering and construction educators and employers.

In construction, workflow variability has been associated with poor project performance such as higher work in process, longer activity durations, and project completion delays. In this paper, we analyze the advantages and limitations of state-of-the-art tools and methods for managing workflow in the field, seeking to understand the importance of measuring the activity flows to anticipate variability in the activities’ execution. We found that current methods are insufficient to help field managers understand the impact that variability in the activity flows has on activity execution, and understand how variability is propagated between activities. As a result, field managers cannot anticipate the impact of workflow variability and rely on their experience and intuition managing workflow variability during look-ahead planning. We propose a theoretically based workflow model that formally represents construction workflow variability to help field managers analyze the in-project workflow variability and its impact on downstream activities. We developed the model by extending existing representations of construction workflow with the mechanisms that cause workflow variability.

This research presents the development of a decision-support framework for investigating the impact of planners’ risk tendency toward construction performance on project outcomes, such as time and cost. The framework finds optimal resource utilization solutions for a portfolio of a projects competing for a limited pool of resources under different sharing policies. It enables planners to select solutions that best fit their risk tendency. To this end, the framework is composed of two models for productivity simulation and multi-objective optimization. First, a productivity simulation model (PSM) is employed to simulate stochastic nature of time-related risk factors on resource production rate using Monte Carlo Simulation. Second, an NSGA-II based multi-objective optimization model is developed in order to simultaneously minimize portfolio completion time and cost. The proposed simulation-optimization framework provides non-dominated optimal time-cost tradeoffs and demonstrates how overall time and budget limitations, as well as risk tendency, can affect the decision making process. It also enables planners to examine the impact of resource sharing and work schedule policies on project outcomes in the context of risk tendencies. An application example is analyzed to demonstrate the implementation and capabilities of the developed framework.

Construction project management is essentially an ongoing optimization process with multiple constraints consisting of scope (quality), time, and cost. These constraints are interrelated and compete with each other, which require an integrated method to express the complete project efficiently. Furthermore, unifying the multi-objective model should maintain necessary details of sub-models and accurately reflect the true situation of the project. This paper provides a unified way to describe the project mathematically with singularity functions, both effectively and efficiently. The contribution to the body of knowledge is threefold: First, sub-models with two constraints are modeled by point-slope formulas which includes linear schedule (time, quantity), cash flow (time, cost), and resource usage (resource, time). Second, the triple objective model is expanded to the point-vector formula with singularity functions without weakening its sub-model’s intricacy and accuracy. Third, all possible models with combinations of the three dimensions are expressed mathematically, graphically, and their possible applications are discussed. Different from 4D CAD and BIM that coordinate rich building information in databases at a practical level, this study explores comprehensively modeling projects in multiple dimensions synchronously at a conceptual level. Such unified quantitative model may lead to new analytical and optimization approaches for construction management.

The design-build (D-B) project delivery has been increasingly used in the highway construction industry because of its documented benefits. Many studies have shown the superior performance of D-B projects when compared to traditional design-bid-build (D-B-B) projects. However, there is a lack of an empirical comparison of project performance between D-B and D-B-B projects. This study analyzed cost performance of 2,766 D-B-B and 210 D-B highway projects from five state Departments of Transportation (DOT), including Florida, Indiana, Ohio, Oregon, and Utah DOT. These projects were classified into five categories in cost: $2 million to $10 million; $10 million to $20 million; $20 million to $50 million; $50 million to $100 million; and over $100 million. The result of the statistical analysis shows that D-B projects statistically performed better than D-B-B projects regarding cost growth for project size from $10 million to $ 20 million, $20 million to $ 50 million, but not for project size from $50 million to $ 100 million. Likewise, there is no statistical significance of cost growth between D-B-B and D-B projects for the project size between $2 million and $10 million and the project size over $100 million. The study is, however, limited in sample size to project sizes from $50 million to $ 100 million and greater than $100 million. Further research should be conducted to validate the findings. State transportation agency officials can use these results to support their decisions when selecting D-B delivery.

Forecasting methods, as a control tool, aim to predict final project outcomes and play an important role in assessing the performance of construction projects. In construction, accuracy has been the most important criterion used to assess forecasting performance. However, in addition to accuracy, forecasting systems must provide timely information about project status so corrective action can be taken when required. Therefore, researchers have introduced timeliness as an additional forecasting performance measure. A preliminary study found that some forecasting methods with good timeliness indices do not necessary predict deviations with precision. Furthermore, the cost predictability indices proposed in the literature do not provide sufficient information upon these two desirable features of forecasting methods (timeliness and accuracy). Currently, researchers and practitioners are looking for appropriate methodologies that allow project managers to assess forecasting performance. This study assessed the performance of construction project cost forecasting methods using data envelopment analysis (DEA), based on timeliness and accuracy indices. Preliminary results showed that there is statistical evidence to indicate that the ranking obtained using DEA differs from that obtained using the traditional forecasting performance indices.

Effective management of the construction phase is essential for the successful completion of a project. Continuous monitoring is required to ensure that construction is proceeding as planned or to correct any deviation from the planned duration or cost. Moreover, data management is essential for effective and timely communication between the project participants. This is particularly important in the construction industry since data related to the project is generated on a daily basis. As noted in previous research effort, managing this data can take considerable time from the project manager. In order to improve the data collection and transmission in the field, a research team from the University of Puerto Rico at Mayagüez presented a mobile application for automating the collection process of field inspection data and reporting using mobile devices. This paper presents a continuation of the previous research effort and presents a web component that complements the mobile application to automate and enhance the administration process for highway projects. This web based system is generated from the daily inspection data collected with mobile devices to create reports and to perform analyses that are critical for project administration and control.

Early identification of utility conflicts and planning for addressing them is critical for controlling time and cost overruns in highway projects. This study introduces a framework for the evaluation and selection of highway routes based on existing utilities in the vicinity of the proposed routes. As a way for comprehensive consideration of utility conflicts early in the planning stage of highway projects, the framework identifies and classifies conflicts, calculates their estimated direct and indirect resolution costs, and ranks the proposed routes based on their costs related to these conflicts. The study introduces a conflicts classification system that incorporates conflict resolution actions (e.g., utility relocation) and the probabilities of the application of these actions in order to determine conflict cost estimates. The framework was implemented using an ArcGIS Model that examines the GIS data related to existing utilities, and analyzes conflicts with predetermined proposed highway routes. The model also analyzes the impact of conflicts on neighboring facilities. A case study is introduced to demonstrate and validate the proposed framework. Sensitivity and uncertainty analysis showed that the utility line length, utility material, and the average annual daily traffic (AADT) of the proposed route are the most significant variables in the cost estimates.

Infrastructure upgrading projects in slum areas face a number of challenges that are not common to those of similar projects in planned areas. These challenges stem from (1) the heavily-congested and unplanned nature of a slum, which results in a series of logistical challenges; and (2) the fact that slum dwellers reside in and around the upgrading project sites, which raises the level of resistance to project execution. As such, slum infrastructure upgrading projects face high risks of prolonged project duration and increased construction costs. This paper focuses on sanitary networks upgrading projects in slum areas. In these projects, the unplanned nature of the slum is manifested in the undocumented variations in road widths and their steep slopes as well as the ever changing urban characteristics of the area. It is common, under such conditions, for contractors to be forced to use different construction methods. Furthermore, these projects are expected to face high levels of dwellers’ resistance because of the socioeconomic disturbance resulting from the construction activities. To address the aforementioned challenges, this paper proposes an automated heuristic-based planning model capable of prioritizing the upgrading phases and selecting the suitable construction methods to accelerate the delivery of service to slum dwellers. To this end, the model computes the rate of service delivery under different construction scenarios and hence accounts for the expected resistance in cases of late delivery. The model processes the urban characteristics of slum areas and topography data as well as the proposed sanitary network design in order to generate the optimal order of upgrading phases and the optimal selection of construction equipment. An application example is presented to demonstrate the use of the model and its capabilities.

Solar photovoltaic (PV) construction projects are expanding and have complex technical requirements coupled with narrow financial margins. Limited research has examined large-scale PV production planning and techniques to capitalize on repetitive activities, prefabrication opportunities, and the high variability of regional factors affecting construction processes. This research examines large scale PV projects to identify factors affecting key planning decisions and methods to support production planning on large scale PV project. Lean construction concepts and instruments are applied to inform an examination of production planning on multiple PV construction case studies. Lessons on these cases are used to inform a deep case study and production experimentation to produce experience and short interval production (SIPS) data. A tool developed to support PV production planning is presented including use of 4D simulation to enhance communication of planning decisions. The results of this research indicate key planning variables impacting design and prefabrication decisions on PV projects and help elevate the needs and opportunities to apply detailed construction planning methods to large scale and highly repetitive PV projects analysis techniques to help inform key planning decisions.

Effective, budget-conscious renewal strategies that improve sewer network conditions and reduce the risk of pipe failure are essential for municipalities in order to maintain acceptable performance and service levels for their wastewater infrastructure assets. Developing such strategies is a challenging task because the wastewater system is a complex system that consists of many non-heterogeneous components. This complexity stems from the fact that the behaviors of these components are impacted by many external factors such as the asset deterioration process. The deterioration of wastewater assets creates significant risks to the surrounding environment due to infiltration and exfiltration processes, and the increased risk of their failure. This creates a pressing need for an accurate deterioration prediction model to forecast the current and future conditions of wastewater systems. Therefore, this paper proposes a multi-objective optimization approach for wastewater network rehabilitation that utilizes both a life-cycle analysis approach and a semi-Markov deterioration model. The main purpose of this model is to answer the following question: how and when should we intervene to optimize the allocation of the renewal budget—by maximizing the average network condition and minimizing the average risk of failure—that is subject to minimum condition limit, minimum risk of failure limit? And how do we prevent maximum from exceeding the available budget within the specified planning horizon?

The economic merit for sustainable power generation from “clean” coal technology largely depends on a plant’s air quality control system. The research in this paper examines “clean” coal technology in the U.S. and presents a case study of the processes for planning and upgrading an air quality control system at an existing coal fired power plant. Continuously striving for ever-improving plant reliability and operational functionality, several sub-systems within the current case study’s air quality control system were identified for potential upgrades and expansion. The work associated with these improvements was performed during a three month plant-wide outage in 2015. The start-up and commissioning of the upgraded air quality control system was dependent on its logical relationships to other critical and near critical work within the plant’s other systems. As existing coal fired power plants continue to be utilized concurrently with the construction of new plants, the economic efficiency for upgrading and constructing other air quality control systems will have impacts on human health and the environment at a global level. This paper presents the key decision points for identifying areas of improvement to air quality control systems and the methodology for planning and executing the work to ensure successful implementation.

Alternative project delivery methods (APDM) have become more widely used in all industry sectors. Although the infrastructure sector has lagged behind the building sector, transportation projects have considerably increased the use of APDMs in the past several years. The increased use of APDM has led to a preconceived perception of benefits when using delivery methods, such as design-build (DB) and construction manager at risk (CMAR), over traditional design-bid-build (DBB) delivery. This paper is the result of a study of more than 80 transportation project owners. It contributes to the industry’s understanding of APDM by analyzing the perceptions of owners on how each delivery method affects project success. The paper also provides a ranking of the factors that influence owners in the delivery method selection process. DBB, DB, and CMAR projects are analyzed using individual t-tests to determine the best delivery method to achieve a specific objective. Study results show owners are highly motivated by controlling cost and schedules for a project, as well as allocating risk appropriately. The paper presents an in-depth comparison of APDM using least significant difference intervals and multiple range tests to assess the significance of the perceptions of transportation project owners.

Delays are frequently reported as the cause of several conflicts that affect the different parties involved in construction projects. In order to avoid these conflicts and succeed on the delivery of construction projects, contractors should implement project management processes. Project management includes a number of planning and controlling processes that are recommended for complying with the owners’ requirements related to project time, cost, quality, and others. The study reported in this paper aimed at assessing the use of processes related to project time management and its relation with project schedule performance (i.e., timely completion). The study included the assessment of fourteen school construction projects executed by a public agency in the Yucatan Peninsula, Mexico. These projects were monitored during the construction phase in order to measure two different variables: the degree of use of processes related to project time management and the project performance regarding timely completion. The results demonstrated there is statistical dependence between these two variables. In this case, it was found the projects that attained timely completion were also those in which most of the recommended planning and controlling processes had been implemented.

Adequate planning is essential to successfully deliver a project. Construction site managers’ “planning” efforts include various levels: (1) identifying the sequence and timing of activities; (2) scheduling and organizing the wherewithal to perform the job; (3) planning with other project teams to coordinate for shared resources and/or interaction; and (4) designing the production system. Although there are several studies on construction project planning and its outcomes, the research pertaining to the effect of the on-site planning during construction on the performance is very limited. This research, via a questionnaire-based survey, identifies the relationship between the site manager’s effort on the 2nd and 3rd levels of planning and the work plan reliability (WPR), measured by the plan percent complete (PPC). The survey was distributed to site managers of general contractors and subcontractors in North Carolina, US. Eighty three useable survey responses were collected and classified based on management level, type of work, and project characteristics. Correlation analysis results show that there is a significant correlation between the efforts at the 3rd level of planning and the PPC average for the subcontractors. Results also show that the PPC variation decreases when more efforts are devoted on the 3rd level of planning.

Temporary haul roads are typically designed and constructed to handle mass earthworks in heavy civil and industrial construction, critical to achieving haulage efficiency and safety in earthmoving operations. Traditional temporary haul road layout design has long been based on experience rather than science. Previous research endeavors developed mathematical models and solution algorithms in an attempt to explore the possibility of analytically addressing temporary haul road layout design problems, while achieving limited progress. This study introduces a grid-based optimization methodology by adopting a mixed-integer linear programming (MILP) model, aimed to generate the optimal layout solution in real-world applications. The optimization processes and results are illustrated with a practical engineering case. A comparison of different layout designs for the same case, including the heuristics-based field design and analytical designs resulting from recent research, shows that the proposed methodology is capable of producing optimal solutions to temporary haul road layout design problems.

Construction projects are sometimes temporarily suspended due to economic, political, and/or social problems that affect their development. This situation usually creates budget increases due to poor planning and an accelerated decision making process. This study proposes that in order to have adequate temporary suspension, it is necessary to plan three stages in detail: the slowdown, the conservation, and the resumption of the project. The objective of this study is the analysis of this process and the creation a guide that would allow a successful temporary project suspension. An industrial project at a cost of approximately $ 300,000,000 US dollars that had to be suspended temporarily when wearing the 70% advance was studied. The suspension was caused by the owner’s financial problems. Each of the three stages was analyzed and the effects on scope, time, cost, quality, human resources, risks, communications, and procurement were reviewed. Experts from the owner organization participated in this study, analyzed the process following the slowdown and conservation, and recommended a restarting process. At the end of the process, a guide was structured which shows the activities and recommendations to be undertaken in each of the three stages. In conclusion, it can be mentioned that it is important to consider many factors associated with the suppliers, contractors, designers, owner staff, budgets, and cash flow in order to guarantee a successful project.

The winner’s curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project. This paper uses game theory to identify the degree of the winner’s curse in two common construction bidding environments; namely, single-stage bidding and multi-stage bidding. The objective is to compare the aforementioned two construction bidding environments, and determine how learning from past bidding decisions and experiences can mitigate from the winner’s curse. To this end, the authors (1) presented the symmetric risk neutral Nash equilibrium (SRNNE) as an optimal bid function; (2) developed simulation models for single and multi-stage construction bidding processes; and (3) analyzed the results of the simulation models, which is based on an actual dataset of California Department of Transportation projects. This research demonstrated that the majority of general contractors and sub-contractors suffer from the winner’s curse in both single and multi-stage bidding environments, and that the SRNNE optimal bid function provides the contractors with a tool to avoid the winner’s curse problem and to consequently gain strategic positive profits in both bidding environments. This research should reduce the industry exposure to the effects of the winner’s curse in construction bidding.

Warranties in the construction industry have become more prevalent as buyers often rely upon the length of the warranties in evaluating products and services. Research has shown that the length of the warranty has no correlation to the actual performance of the product or service being purchased. The purpose of this paper is to present the findings of a study, seeking to optimize the value of a warranty as a risk minimization tool. In the research, a warranty program was developed and tested with a medium-sized construction coating manufacturer. The program tracks all the installed projects and provides an overall snapshot of the performance of all the installed projects and identifies “risky projects” (leaks, blisters, end-user dissatisfied) via annual collection of post-construction performance data. Five years of data are presented with 68 risky projects being identified with an average risk resolution of less than 30 days. The paper presents the development of the warranty risk minimization program, the risk identification and resolution methodology, a new model for measuring performance in the construction industry and the optimal use of warranties.

Transferring life-cycle information to facility owners at project close-out for use in post-construction activities has become increasingly critical and needed to allow them to better manage and effectively maintain their facilities. This case study investigates the specific challenges that a large educational institution in the United States is having in terms of determining facility information for use during facility operations and management. The current process is tedious, time consuming, and error-prone, leading to information that is often inconsistent or incomplete. Process improvements utilizing industry standards and BIM are being examined and evaluated to determine which may be the best options for the University with the end goal being developed BIM guidelines and a Facility Information Management Guide that best fits the University’s needs. The paper summarizes BIM-FM requirements of other large owners and how some experienced contractors have helped to overcome common challenges. The specific challenges related to the University and potential methods for overcoming those challenges are also presented. Lastly, a strategy to pilot test the process improvements is discussed with future strategies for successful adoption.

Spatial conflicts that occur on a construction site seriously increase the probability of struck hazards. Traditional 2D-based site safety plans have significant limitations in addressing the dynamic nature of on-site object collisions. Current site layout practices rely heavily on 2D drawings and past ad hoc experience, which lack the capacity to dynamically detect, demonstrate and analyze workspace interference on construction jobsite. In this paper, the authors introduce a construction safety assessment model using statistical 4D BIM-based workspace conflicts detection method for evaluating on-site collisions. Workspaces of on-site objects are modeled and visualized, and the generated 4D collision data are statistically analyzed. The proposed model contains three procedures: (1) 3D workspace representation, (2) Spatial-temporal conflicts detection, and (3) 4D site safety analysis. Implementing this model into a virtual environment achieves interactive conflicts visualization and dynamical construction safety simulation. Based on this framework, managers could assess site safety of construction plan during pre-construction stage to proactively reduce the potential struck hazards and improve the productivity of construction site utilization.

In fast track projects, the construction time of the concrete building structure is highly affected by the amount and system of available formworks. However, formworks are usually very costly and providing extra formworks significantly increases the project costs. On the other hand, making full use of available resources, including formworks, requires appropriate planning of construction activities. In this research, a framework is proposed to use building information modeling data, sequencing logic and simulation to automatically determine how the amount of available table forms affects time and cost of building projects with respect to workspace spatiotemporal conflicts and available paths for transporting tables between different floor zones. Moreover, this framework provides the best schedule for a given amount of available table forms that minimizes the project duration. The proposed framework was tested in a case example, an eight-floor fast-track commercial building project with an area of nearly 18,250 m².

In early stages of fast tracked construction projects—such as mega industrial projects—contractors do not have access to detailed and final drawings. Yet, they usually need rough estimates of material quantities in the project. They usually depend on initial versions of BIM models that contain all trades (e.g., pipes, steel, etc.) information. Because they are premature models, 3D object attributes may be missed, omitted, inconsistent, or incomplete which makes extracting quantities from them a manual, time-consuming, and inaccurate task. In this research, we aim to automate this task by applying shape recognition techniques on unlabeled 3D models. These techniques have been used in different fields such as handwriting recognition, object recognition, and 3D object search and retrieval. We experiment with some of these techniques to estimate material quantities for industrial construction projects based on the geometry of unlabeled 3D objects. We investigate a number of these techniques keeping in mind that the main usage will be a preliminary estimate in early stages of the project; therefore, we favor fast techniques with a margin of error over more accurate—but relatively slow—ones. This paper discusses applicability of these techniques and shows the results of applying and testing them on real industrial construction projects from a partner contractor.

Building information modeling (BIM) is a major upheaval in construction industry. Although BIM advantages in construction management has been proved in many papers reviewed, there are still many limitations that inhibit organizations to use BIM models efficiently. However, advancement of application programming interface (API) in the recent years has facilitated employing BIM in construction industry. This research contributes to the state of practice in construction management by developing API codes to automate estimation of construction. The developed API automatically filters items related to a particular discipline within a particular work area and facilitate systematical quantity take off in different work areas. This allows the planner to define proper 3D work areas and to estimate the required materials and man-hours in different work areas throughout the project. The results of the developed automated approach are compared with hand calculated results as well as the results calculated from the BIM software interface in special cases. Finally, the research has been validated by a case study of an actual petrochemical project.

The building information modeling (BIM) technology is a methodology that allows the exchange of information efficiently; it is used by stakeholders who participate in the building life cycle. This methodology is successfully utilized for new buildings; however its implementation in heritage architecture is still emerging. The purposes of this research are to perform an analysis on the BIM state of the art for existing buildings, to identify the knowledge gap in historic buildings, and to propose a theoretical method for heritage architecture management. To achieve these objectives, a total of 65 articles and papers have been analyzed. These documents were grouped into five categories, and conclusions were drawn. Furthermore, a theoretical method was proposed by reviewing the BIM literature and by inventorying the monument’s needs. The results illustrate the emerging state of BIM for heritage architecture, also called historic building information modeling (HBIM); there are few practical applications so far, including database management of the historic monuments. The analysis aimed to propose a method to manage architectural patrimony. In conclusion, the creation of this HBIM method for movable and immovable heritage analysis, research, documentation, and management represents progress in how the exchange of information occurs between the different professionals and stakeholders involved in architectural patrimony. This research is part of the scientific project entitled: The Design of a Database, Management Model for the Information and Knowledge of Architectural Heritage; HAR2013-41614-R, subsidized by the Spanish Ministry of Economy and Competitiveness through the National Programme for Research Aimed at the Challenges of Society

Mobile BIM is an emerging trend in the architecture, engineering and construction (AEC) industry. The ubiquity of modern mobile devices allows users to access building information readily in the field using touchscreen interfaces, while built-in multi-sensors provide the ability for intuitive 3D navigation within the Building Information Model (BIM) environment. Despite the apparent advantages of universal access, the need to efficiently locate users in BIM environments and retrieve information they need in mobile contexts presents significant challenges. For jobsite workers, frequent touch manipulation, often wearing gloves, is challenging and inconvenient in on-site environments. Visual-sensor-based context-aware information computing offers a promising alternative to circumvent such issues. Taking advantage of omnipresent paper drawings in current on-site work practices, this research develops a mobile application, BIMap that utilizes physical plan drawings as Tangible User Interfaces for user localization in 3D BIM models. Through visual registration methods, various sections of drawings are identified as natural markers, containing corresponding BIM identification information such as drawing type, structure height, building floor, and CAD layer identifiers. The relationship between planar natural markers (i.e., drawings) and the planar device screen space is represented via a homography matrix. After this registration process, mobile users can readily localize themselves in BIM models by pointing out specific locations on plan drawings in the view of the mobile device’s camera. The feasibility of the proposed approach was evaluated through four case studies, representing BIM models of various scales.

Building information modeling (BIM) is progressively being adopted for precast construction as it facilitates efficient design, manufacturing and construction through generation and management of the virtual models incorporating both the physical attributes and functional behavior. Several studies have addressed the application of BIM for precast construction; however, these studies have focused on specific areas in the precast project lifecycle such as construction and inventory management. This study focuses on the systematic review of the potential applications of BIM through the entire precast project lifecycle. The stages in the lifecycle considered are “Concept,” “Design,” “Planning,” “Production,” “Logistics,” and “Erection/Construction” which are similar to stages of a manufactured product. This study reviews past work in the domain and identifies areas for future work. The benefits and shortcomings of BIM application are evaluated based on the needs of each area and the features available in current BIM software. This paper provides a detailed review of the state-of-art of BIM application for precast construction lifecycle and potential for integrated BIM implementation in practice.

In its current state of change, the U.S. healthcare industry is placing a focus on medical process efficiency, which contributes to architects’ motivation toward evidence-based design. Noting that computer simulation is gaining acceptance as a source of evidence, the authors have set out to develop a layout evaluation by simulation protocol to evaluate the process efficiency of a healthcare facility as influenced by that facility’s layout. The authors have chosen a system-of-systems analysis for developing a ‘healthcare delivery’ model and computer simulation of an existing medical facility. The situation of an acute care unit that has operated in two different layouts in an existing hospital has created the opportunity to evaluate outcomes for both an “existing” and a “proposed” facility. A matrix of simulations will be evaluated considering simulation technique versus level of detail. The case study can provide a strong validation not only of fidelity to the existing state but also of the simulation’s power for predicting the influence of a future design layout.

Renovation projects play a critical role in upgrading the existing infrastructure and preserving the historical landmarks. Major challenges in the current renovation projects include lack of up-to-date facility geometric information and seamless integration of the proposed renovation with the existing parts of facilities, and the unforgiving requirement/ imperative to integrate technology upgrades without disturbing the historical fabric of the landmark. Availability of updated geometric information is important since it reflects the as-is geometric and spatial information. Building information modelling (BIM) and laser scanning have been used to overcome these challenges within the current practice. However, further process improvements, a working understanding of historical construction methodologies and a hands on investigation are required in these two technologies to establish repeatable processes as best practices for future projects. Challenges in using these technologies in the current practice have been analyzed in an iconic landmark renovation project. The case study was done on one of the largest gothic style cathedrals in North America which is currently undergoing a mechanical system renovation. The research team was involved in the mapping of the existing facilities, aligning the proposed systems with the existing systems and overcoming the spatial conflicts while implementing the proposed renovation. This paper highlights the challenges faced by the construction team as they worked on the ongoing renovation and the lessons learned from modelling and laser scanning of a legacy facility.

A lack of communication has been criticized as an impediment to improving the common understanding of building requirements among the key stakeholders in the AEC industry. Recent developments in virtual reality (VR) have encouraged the utilization of interactive architectural visualization in the design, construction and facility management of building projects. Nonetheless existing efforts concentrate only on improving the single person experience, aiming to make sense of facilities. This paper finds that the interpersonal interaction in the VR environment is more critical to the effective communication in a building project, as it creates a shared immersive experience. To prove the concept, a BIM-enabled VR environment named “multiplayer virtual walk” (MVW) was developed to realize multiplayer walkthrough in a yet-to-finish office building. MVW allows real time interactions of remotely located project stakeholders in the same environment, with a shared immersive walkthrough experience. Unity 3D is used to create an interactive environment, and photon server is used to implement the cross-platform multiplayer function. A pilot study has shown that MVW facilitates project teams to examine requirements before the commencement of construction works, and ultimately encourages a shared vision on the project.

Structural monitoring of built assets, performed to ensure safety, usability, and to better understand the assets’ structural behaviour, is becoming common practice. One method to monitor structural behaviour is to use fibre optic systems to measure temperature and strain in structural elements. Fibre optic systems are relatively inexpensive, easy to install, versatile, and widely applicable. However, all the data acquired by monitoring systems are meaningless if they lack context within the built asset, and they are of little use if they cannot be appropriately exchanged and visualised. This paper presents an extension to the IFC data model standard for structural monitoring systems. The extension allows (1) to model structural monitoring systems, (2) to store and retrieve acquired data, and (3) to visualise the data directly on the BIM model. The James Dyson Building at the University of Cambridge is used as a case study, in which selected reinforced concrete columns, beams, and slabs have been instrumented with two types of fibre optic sensors. The case study demonstrates that the extension is able to fully describe the fibre optic monitoring system and that it can aid in its design, deployment, and further operation.

The successful implementation of Building Information Models (BIMs) for facility management, maintenance, and operation is highly dependent on the ability to generate such models for existing assets. Generating such BIMs typically requires laser scanning to acquire point clouds and significant post-processing to register the clouds, replace the points with BIM objects, assign semantic relationships and add any additional properties, such as materials. Several research efforts have attempted to reduce the post-processing manual effort by classifying the structural elements and clutter in isolated rooms. They have not however examined the complexity of a whole building. In this paper, we propose a robust framework that can automatically process the point cloud of an entire building, possibly with multiple floors, and classify the points belonging to floors, walls and ceilings. We first extract the planar surfaces by segmenting the point cloud, and then we use contextual reasoning, such as height, orientation, relation to other objects, and local statistics like point density in order to classify them into objects. Experiments were conducted on a registered point cloud of an office building. The results indicated that almost all of the walls and floors/ceilings were correctly clustered in the point cloud.

With the fast advancement of information technologies (IT) in recent decades, the informatization level of various industries is enhancing. In the construction industry, building information modeling (BIM) has created a new evolution in planning, progressing, and managing construction projects. However, access to BIM software (e.g., Navisworks) on a remote construction job site is not always feasible. This study aims to develop a smart controller that can portrait the current snapshot on a job site based on the GPS data received from a portable device (e.g., smartphone). This controller captures a rendered high quality image by mapping the GPS based data and the model’s local coordinates together. Finally, such images would be reported to the crew on the site via cloud saving systems. The developed results facilitate employing the BIM model for different people involved on a job site (e.g., crews, foremen) for employing BIM for planning, building, controlling, and progressing purposes.

Digital models incorporating 3D geometry and information attributes are becoming a standard for industrial facility and commercial building design. Despite the advancements in technologies to develop design models, there is an opportunity to better leverage models within facility construction processes. Moreover, there is a need for a comprehensive understanding of construction information requirements that are not presently available, but could be delivered via a model. This presents an opportunity to create coordinated industry guidelines and a process for leveraging model content through the construction phase. This paper presents a taxonomy for digital model uses within construction. Focus group workshops and case studies were applied to capture the current model uses and to identify unmet model use opportunities. Data was collected, analyzed, and compiled into a structured catalog of construction model uses, along with the benefits and barriers to implementation. The taxonomy provides an approach to help an organization identify current model use status and the near-term future opportunities of using virtual models to further meet construction needs. The paper concludes with discussion of how a planning approach and implementation guidelines will be developed.

Human behavior is one of the most important but hard to define factors in emergency escape simulation. Most existing evacuation simulation models are based on agent based modeling (ABM). The foundation of ABM is the assumptions of the agents’ behavior. However, most of the time it is almost impossible to collect human behavior during real emergency scenarios, resulting in a lack of credibility of ABM simulation results. In the previous study, a framework to use a cloud-based immersive online game to solicit human behaviors has been proposed. This paper aims to present a pilot immersive game developed on a small scale in order to validate the framework for mass population behavior data collection. Building information modeling (BIM) is used as the game building environment. Unity is used as the game engine. For this pilot study, the player’s evacuation time, evacuation route, delay time, and congestion are recorded. This pilot game will validate the feasibility of a large scale online game.

This paper presents a dynamic meta-network modeling framework for integrated project performance assessment under uncertainty. In the proposed framework, construction projects are conceptualized as meta-networks composed of different types of nodes (i.e., agents, information, resources, and tasks) and links representing interdependencies between these node entities. The impacts of uncertain events on construction projects are translated as perturbations in different nodes and/or links in project meta-networks. The uncertainty-induced perturbations cause decreases in project meta-network efficiency, and ultimately cause project performance deviation. The ultimate performance deviation is investigated based on two project properties: (1) project vulnerability (i.e., the decrease in meta-network efficiency under uncertainty-induced perturbations); and (2) project adaptive capacity (i.e., speed and capability to recover from uncertainty-induced perturbations). Then, project planning strategies are evaluated based on their effectiveness in mitigating the negative impacts of uncertainty by reducing project vulnerability or enhancing project adaptive capacity. The application of the proposed framework is demonstrated in a case study of a complex commercial building project. Different scenarios related to uncertain events and planning strategies are simulated in the case project. The results of the case study show the capability of the proposed dynamic meta-network modeling framework in: (1) quantitative predictive evaluation of the impacts of uncertainty on project performance; (2) ex-ante evaluation of the effectiveness of planning strategies in mitigating the negative impacts of uncertainty on project performance; and (3) capturing the complex interactions between various tasks, agents, information, and resources in evaluation of project performance under uncertainty.

Cost overrun in construction projects is a critical challenge for both owners and contractors. Budgeting for cost escalation is a major concern in the planning stage of these projects. This study aims to introduce a systematic approach to model the cost escalation factors in the construction projects. First, it is proposed to use the ENR construction cost index (CCI) as a measure of the construction cost escalation. Then, using GARCH family models, variance or so-called volatility of the escalation factor was calculated. Finally, having the estimation of the volatility of the escalation factor, and using the Value at Risk (VaR) technique, we calculate the VaR of the escalation factor. VaR, in its most general form measures the potential decrease or increase in value of a risky asset or a price index over a defined period for a given confidence interval. This calculation assures cost estimators that the actual value of the escalation factor will not exceed more than the calculated one within a reasonable bound. Planners and cost estimators can use this systematic approach for budgeting and calculating the escalation factor and risk premiums in a more conservative way.

Thermal conditioning of indoor air is basically related to heat transfer between inside and outside of buildings, which influences occupants’ thermal comfort and the associated energy consumption for space conditioning. One significant parameter used in thermal analysis is the heat transfer coefficient of building envelopes. Considering deteriorated conditions of existing buildings’ façades, modeling a building element with a constant surface-wide thermal property based on standard material databases is most likely to overestimate energy performance of existing buildings. This paper evaluates the impact of decreasing the gaps between the architectural information in the as-designed BIM and the as-is building conditions on the BIM-based energy modeling and analysis for existing buildings. By leveraging 3D thermal profiles reconstructed from 2D thermal images, the as-is thermal resistances are first characterized at point-level in 3D. Then, by mapping 3D point cloud data to the meshed BIM, the as-is thermal resistance values are automatically matched with the corresponding BIM element in gbXML schema. The updated BIM model is used as an input of energy simulation engines, and finally the Monte Carlo analysis based on the orthogonal sampling is conducted for impact analysis. Experimental results on several case studies validate the significant potential of reflecting the as-is building conditions from thermographic inspection in energy modeling, especially for buildings with wall degradations in the cold region. This provides building practitioners with the insight on how to improve the reliability of building energy analysis.

Building information modeling (BIM) users in architecture, engineering and construction (AEC) have previously used virtual reality (VR) environments to support coordination and information visualization. With recent advancements in inexpensive and ubiquitous computing power, BIM users have access to multiple types of VR systems ranging from non-immersive desktop VR to immersive head mounted systems and immersive CAVEs. These systems have different strengths and weaknesses. Many users face the question on which VR technology to adopt in a given context to get the most value. In the study described in this paper, we will evaluate three different types of VR environments, namely Oculus Rift VR, Cardboard VR, and CAVE VR, in their ability to support tasks driven by construction project management related use cases. We also compare these environments in terms of ease of generation and deployment of facility models and interactions with such models, as well as their usability that is conducted through a user study associated with a construction project. The results from this study indicate significant variation in users’ performance in different tasks using different environments. This research study can potentially contribute towards formalizing selection and integration of appropriate VR environments in support of construction project management.

The National Electrical Manufacturers Association (NEMA) promotes effective daylighting responsive electric lighting and controls in the built environment as a means to increase energy efficiency. Integral to effective daylighting design and responsive controls are daylight performance metrics that measure and quantify effectiveness. Interrelated to effective daylighting are the concepts of spatial and temporal uniformity associated with daylight availability in a space. This research develops and tests related metrics by running a set of representative daylighting models to generate sets of data characterizing daylight conditions ranging from uniform to highly dynamic both temporally and spatially. Results of these simulations explore the extent to which proposed metrics accurately describe and assess daylighting uniformity. This research is part of an effort to develop a comprehensive daylighting code that describes base levels and quality of daylight required for various built spaces with the ultimate goal to inform the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) 189.1 Standard for the Design of High-Performance Green Buildings.

A business commute footprint is a significant factor related to buildings and infrastructure. Commute trips constitute a major portion of daily traffic; causing traffic congestion, travel delays, greenhouse gas and air pollution emissions, and traveler stress. In addition, daily commutes consume significant portions of traveler budgets and impacts traveler health. This paper presents the results of an exploratory pilot study performed at two universities, University of Colorado Denver and California State University, Fresno. In this pilot study, undergraduate students in four engineering classes completed a two-day travel survey eliciting their commute choices and preferences to and from campus. This paper presents the analysis and discussion of these survey results. It uncovers student commute patterns and explores preferences and potential incentives to understand commute travel mode values, concerns, and choices. Initial findings reveal high reliance on individual automobiles as well as highlight significant opportunities to optimize student commute footprints. These findings support additional publications, where a geographical information system (GIS) model is developed and integrated with an optimization model to identify the optimal selection of business commute alternatives (e.g., driving automobile, carpooling, using public transportation, biking) that minimize business commute footprint based on individualized commute incentives (e.g., monitory incentives, burned calories, saved commute time).

In recent years, building information modeling/model (BIM) has demonstrated its return on investment in design and construction. As a result, the Architecture, Engineering, Construction and Operations (AECO) industry has expressed great interest in the use of BIM for facility management. Ideally, BIM models should serve as a full database for different purposes and functions during the project life cycle. However, in reality different stakeholders in the AECO industry are still working in their own silos and are hesitant to collaborate with each other. Unfortunately, facility owners are forced to pay extra costs to repurchase the as-built data for their facility management software after project delivery. Ironically, most of this data was already delivered to them in the BIM models. This paper aims to validate the feasibility of using BIM to populate facility maintenance software automatically in order to avoid unnecessary costs for facility owners. A case study of an administrative building is conducted for this database automation process. In addition, the case study compares the cost of BIM supported facility maintenance software population with the traditional assessment method. Potential savings of using BIM for database automated population are investigated.

The unique nature of construction projects and uncertainties during project execution make construction a highly risk-prone industry. The system dynamics (SD) approach, which focuses on the cause-effect relationship of model variables, is a viable option to model and analyze construction risks, which are considered to be highly dynamic, and has the ability to capture the interrelationships and interactions among different risks. However, conventional SD has a limited ability to handle risk imprecision and uncertainty; these elements can be best dealt with using fuzzy logic. Research endeavors to integrate SD and fuzzy logic so as to address the shortcomings of SD in construction risk modeling and analysis are very few. In this paper, a methodology for developing a fuzzy system dynamics (FSD) framework is proposed that combines the strengths of SD with those of fuzzy logic to improve construction risk modeling and develop risk mitigation strategies. The main contributions of this paper are: (1) identifying research gaps in FSD modeling; (2) providing a systematic and detailed methodology for developing the FSD framework; and (3) examining existing approaches for representing fuzzy variables and fuzzy rules, and the impact of different fuzzy arithmetic operators and defuzzification methods in FSD models.

There are three major risks associated with tunnel construction projects: limited information on site conditions, selection of the appropriate construction method, and the occurrence of unpredictable events. Firstly, underground conditions cannot accurately be predicted, since geotechnical investigation merely provides a forecast of underground characteristics. Secondly, unforeseen ground conditions may drastically affect the selection of the construction method. Thirdly, seismic activity may threat the project´s feasibility without warning. Therefore, tunneling projects are inherently risky and uncertain with significant consequences in the project´s financial viability. Consequently, this paper attempts to assess and valuate the risks inherent to tunneling projects so as to consider their impact on project´s free cash flow estimation and the return on invested capital. The methodology employed to assess risks included Monte Carlo simulations and statistical methods. The results were obtained using data from an actual project currently under construction in Colombia. The findings suggest cost uncertainty is primarily determined by quantity variations during construction. Furthermore, the degree to which quantities vary is dependent upon previous field sampling thoroughness and the extent of geotechnical investigation during the project´s initial stages.

Many construction projects have adopted building information modeling (BIM), and BIM-based information has become extensively compatible with various sources of external data for various objectives including facility maintenance work; however, the condition assessment process for facilities has a significant shortcoming in that there are several inefficiencies involved in the use of BIM-based information. In this paper, we propose an approach for BIM-based condition assessment that considers data interoperability. To that end, we defined a condition assessment process thru four main steps, with four key algorithms, to ensure BIM-based data interoperability. The proposed approach will enable facility managers who are responsible for component conditions to use a BIM-based model directly in the facility management system without having to do additional manual data input. The proposed process helps to ensure that the data input required for facility management is more accurate and reliable through a computerized data management approach.

The utilization of BIM and IFC allows project participants to collaborate across different areas by consistently sharing interoperable product information represented in a model. Comments or markups generated during the coordination process can be categorized as communication information, which can be shared in less standardized manner. It can be difficult to manage and reuse such information compared to the product information in a model. The present study proposes an interoperable coordination method using BCF (the BIM Collaboration Format) for managing and sharing the communication information during BIM based coordination process. A management function for coordination in the BIM collaboration system is developed to assess its ability to share the communication information in BIM collaboration projects. This approach systematically links communication information during the coordination process to the building model and serves as a type of storage system for retrieving knowledge created during BIM collaboration projects.

The goal of this study is to use modeling and simulation of evacuation plan (MSEP) to support management to execute proper evacuation plan in case of emergency at Hancock Stadium in Illinois State University. The analyses of research literature and evacuation scenarios are used to build a simulation system that will connect a description of emergency and evacuation situation with its context. It shows how an evacuation plan actually functions. The results of the study will help safety managers to understand the current situations of evacuation according to the specific simulation scenarios. The evacuation simulation is categorized according to the types of emergent situations and the number of evacuees. Each simulation is validated according to the real time data collected through observations and the published literatures. The practical implications of the research include the developed system to be used in case of emergencies and the experience gained will help future design of large-scale venues and facilities. This research innovatively used building information modeling (BIM) technology, Dijkstra and open cleared path algorithms, and pedestrian dynamics simulation system for an open-space, steel structure in evacuation routes.

A report published by the National Research Council defined the research on understanding and quantifying the value of infrastructure systems to the stakeholders—and how this value is impacted by the various planning and design decisions—as a national imperative. There is still a lack of understanding and formalized modeling of what different stakeholders value (e.g., energy conservation) in infrastructure systems and how to valuate (i.e., quantify the worth) infrastructure systems based on these values. In prior work, the authors proposed an axiology [theory of value (worth)] based mathematical model to quantify the value of an individual building component. In this paper, the authors propose a mathematical model for quantifying the value of a building system based on the values of the individual components that comprise this building system, and how these components physically integrate together. The proposed model is theoretically grounded in axiology and integration theory. It was initially validated through a case study. The model could provide more explicit understanding of how the components of a building system physically integrate together to fulfill stakeholder values and could facilitate value-sensitive decision making by embodying stakeholder values into project planning towards better synergy between human values and the built environment.

Competitive bidding is the major mechanism for allocation of construction projects and consequently price determination of the construction services. Risk attitude has been recognized as a competitive characteristic of construction contractors primarily due to the risky nature of construction projects as well as competitive bidding. This paper aims to investigate the importance of risk attitude on business success of contractors and mainly to determine the optimal relative range of risk-averseness that helps a contractor outperform its competitors in a long run. An agent-based model (ABM) of the construction bidding environment is utilized to run a variety of experiments where the simulated market is comprised of heterogeneous, risk-sensitive contractors competing over projects with different characteristics. Results suggest that risk attitude has considerable impact on bidding performance of contractors and moderate risk averseness is the optimal policy. Depending on level of cost estimating accuracy and project execution skills of contractors, slightly and extremely risk averse contractors can close the gap between their performance and that of moderately risk averse ones.

Table forms are highly flexible and mobile form systems for beams and slabs. In this system, the components with standard dimensions are fabricated in production plants, transported to construction sites and assembled for final use. Then, based on the plan of the floor zone to be constructed and location of columns, beams, and openings, the assembled tables are placed next to each other and the gaps are covered to provide a perfect surface for concrete casting. However, since the tables are rectangular with standard dimensions, even the best layout does not guarantee the full coverage of the area with tables. The more the uncovered areas are, the more the extra formwork cost and required on-site effort would be. Furthermore, during the construction of a facility, the tables are transported to different zones and floors and used several times. In this paper, an Integer Programming based optimization framework is proposed to determine the best set of standard table dimensions and their layout considering the plan of all the floor zones that they cover in a construction project. BIM model is used to automatically extract required information about geometries of building elements. The proposed framework was tested in a case example.

Construction is typically spread across large areas and requires the remote collaboration of multiple resources, characteristics that create logistical challenges for obtaining a real time overview of operational status. This situation of limited perspective can lead to delayed and myopic decision making about the operation. We present a software platform that provides a dynamically updated operational overview of a construction operation by combining sensors with the operational context provided by a discrete event simulation model. The marriage of these technologies goes beyond data collection automation, as evidenced by its enabling of both real time monitoring and control for construction operations. This paper describes the software interface for developing simulation models which can be repurposed to provide real time operational overview. We also describe the tests that were performed to validate our methodology for updating the model through communications from external entities representative of construction resources.

Construction cost index (CCI) is calculated monthly and published by Engineering News Record (ENR). It is the weighted average price of four major construction components from twenty cities in the U.S., and it generally represents the level of cost for the construction industry. It has, therefore, been widely used for cost estimation, bid preparation, and investment planning. Cost estimators and investment planners, however, are not only interested in the CCI at present, but are also interested in forecasting and predicting the value of CCI in near future. Although CCI increases over the long term, it is subject to considerable short-term volatilities, which make it problematic for cost estimators to accurately bid for construction projects. The accurate prediction of the construction cost can result in more accurate bids and avoid underestimation or overestimation. This paper presents a method that utilizes economic variables to predict CCI by applying the modified K nearest neighbors (KNN) algorithm. It first identifies several economic variables that are highly correlated to CCI and collects the data for the period from 1994 to 2013. Then it splits the data into two parts: the first 95% of the data as the training set and the last 5% of the data as the test set. Finally, prediction models are developed by the training sets and tested on the test set to measure the prediction performance. Experimental results show that the modified KNN algorithm yields very small prediction error under the given dataset. It is anticipated that the proposed method will be useful for predicting CCI and several other construction time series indices in the short term.

Construction production systems are complex in nature and possess a high level of uniqueness. Modelling and simulation of such systems is challenging due to the randomness, complexity, and interdependency associated with many factors such as the type of product created, the steps of the production process, and the medium or the environment hosting the production process. These factors represent or control the working behaviour of a construction system and need to be realistically represented in a model in order to achieve accurate replication of real system behaviours. However, modeling and simulation of these factors require either a rich real life data set, which is seldom available for construction operations, or random generation of complex data structures with highly correlated attributes. This paper presents an investigation of mathematical techniques that can be used to generate random complex data structures while preserving the correlations between the embedded attributes. Generation of weather and pipelines data sets are selected in this study. We propose a non-parametric approach in the weather generation; its performance is measured against a parametric approach. For the generation of pipeline data sets, we propose a generation methodology based on a Markov chain model for a pipeline structure. It represents part of an ongoing research. A detailed description of the methodology and the progress in this part of the study are discussed.

Simulation in construction management, whether Monte Carlo sampling-based, implemented in the program evaluation and review technique (PERT)—a probabilistic scheduling method that handles variable activity durations, or used in other applications, relies upon probability density functions (PDF) of little realism like uniform, simplified like triangular, idealized like normal (or Gaussian), or flexible like Beta. But none of these take into account the nature of the system that they model, whose behavior is created by various interactions between project participants in a network-based schedule. This paper therefore examines the recently introduced so-called Tracy-Widom distribution; derived to mathematically describe the unique behavior of systems that move from mild to intense interactions among their participants, which cause a phase change toward a different behavior. Such characteristics are also found in construction schedules, where a non-critical activity may be briefly delayed without affecting the entire project, or causing to overrun the deadline if critical activities are delayed. The distribution is asymmetrically shaped and steeper on the left side than on the right side. In other words, larger values are more likely than shorter values when sampling. This matches the time extension-prone construction industry and will allow more realistic modeling.

One important need in the construction industry is to visualize the project during its life cycle phases, enabling stakeholders to change/adjust the project before spending more money for unwanted details. Building information modelling (BIM) and 3D laser scanning are two examples of rapidly evolving technologies that can help achieve this purpose. Researchers are conducting an ongoing study to: (1) determine the general knowledge state for these technologies among construction project management personnel; (2) determine the benefits and shortcomings of these technologies; (3) explore barriers to and enablers of these technologies and compare the barriers and enablers with those found in earlier studies; and (4) identify awareness and demand statuses of using these technologies. The study utilizes a survey questionnaire distributed among project management personnel. The research results show that there is generally a high level of knowledge about BIM among construction management personnel; however, this is not the case with 3D laser scanning. BIM and 3D laser scanning help in resolving issues on construction jobsites and there is increased demand for using BIM in the industry; however, amongst the owners surveyed, there is not as much demand for using 3D laser scanning. A lack of experts and high cost are the most cited reasons for not using these technologies on a wide range of projects. The findings contribute to the knowledge in this research area by providing additional information about the current practice related to BIM and 3D laser scanning technologies along with evidence of the current barriers to further diffusion of the technologies throughout the industry.

Accurate and rapidly produced 3D models of the built environment from point cloud data can be used in a variety of engineering applications. When performed manually, this task is often time consuming and labor intensive. In response, several research groups have recently focused on developing methods for segmenting point cloud data based on appearance and geometric information into distinct subsets, and populating the scenes with surface objects. However, these methods, particularly where building systems are in close proximity of architectural/structural elements, still result in over-segmentation or require significant fine-tuning to produce acceptable results. To overcome these limitations, this paper presents a new procedure that takes in a point cloud—segmented at a user-desired level of abstraction—as an input and by considering neighborhood context via a Markov Random Field optimization framework, labels each distinct subset with semantic (wall, ceiling, floor, pipes) and geometric (horizontal, vertical, cylindrical) categories. Experimental results, using real-world point cloud data, show that the method achieves the state-of-the-art performance on semantic and geometric labeling of point cloud data. It is also shown how understanding semantic regions in point clouds—improved via geometric labels—can facilitate the process of generating as-built 3D models from point cloud data.

Shield cutter head failures exist in almost every tunnel project. A systematic approach that incorporates dynamic fault tree (DFT) and discrete-time Bayesian network (DTBN) is developed in this research, in order to facilitate the analysis for shield cutter head failures in tunnel construction. A causal network model is built to simulate the behaviors of shield cutter head failures over time during the tunnel boring machine (TBM) operation. One of the tunneling projects recently completed in the Wuhan metro system in China is selected as a case study to demonstrate the applicability of the developed approach. Results indicate that the developed approach is capable of predictive analysis for the estimation of the TBM performance, and diagnostic analysis in case a low performance or a failure is observed. This approach provides a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions in a complex project environment.

The usage of building information models (BIMs) is well established within the architectural, construction and engineering industry to improve design, engineering and construction processes and exchange project information. BIMs proved to be beneficial by enabling off-site fabrication, reduction (even elimination) of change orders and facilitating access and exchange of digital information beyond the construction phase. This resulted in exploring the potential of utilizing BIMs for assisting facilities management (FM) tasks on a daily basis. Although there are mechanisms (e.g., COBie) to enable delivery of design information to the FM phase, the challenge is the need for customized facility information for operators when they perform FM tasks such as space management, and maintenance. Previous studies showed that BIMs should be customized for the requirements of an FM task at hand. Towards that vision to automatically customize BIMs for an FM task, the objective of the study is to identify the facilities operators’ requirements on as-built BIM, with a focus on the maintenance of HVAC systems. The study builds on the level of development (LOD) and industry foundation classes (IFC) efforts to depict the level of detail of the geometric, spatial and semantic facility information during requirements elicitation phase with facility operators. The paper reports the initial findings of the requirements and LOD of the information for HVAC system maintenance.

Previous research proved that applications of computer vision-based methods for automated recognition and tracking of project related entities at construction sites are very promising. Nevertheless, the applications of these methods for indoor construction sites have not been explored sufficiently. Automated visual recognition of indoor project-related objects can provide both essential information about the current state of progress and provide semantic information for model-based approaches. There are a large number of challenges associated with indoor visual recognition, such as illumination and changes in viewpoint that significantly reduce the accuracy of existing methods. In this paper, a novel procedure is introduced that takes an integrated color and shape-based approach toward recognition of project-related objects including structural elements and interior walls under moderate to extreme illumination conditions and from different viewpoints. The procedure is validated using a comprehensive library of indoor digital images under different illumination conditions and viewpoints. The results indicate the applicability of the proposed method for visual recognition of indoor project-related objects for further use in automated progress monitoring and providing as-built 3D models with supplementary semantic information.

Engineering design of a drainage tunnel specifies start/end positions, alignment, grades, and tunnel diameter/lining material. Tunnel construction faithfully transforms engineering design into the built structure in the underground space. In construction, the guidance system of the tunnel boring machine (TBM) is crucial to ensure all design specifications are met within tight tolerances. The laser-based guidance system has been the mainstream solution in practice. However, laser system setup and calibration demands experience and expertise from a survey crew and also results in frequent construction shutdown over extensive time periods. In (1) straight tunnel sections with highly restrained survey window or (2) curved tunnel sections, the line of sight between the laser station and TBM can be easily blocked. Thus, tunnel construction has to be more frequently shut down solely for the sake of calibrating or relocating the guidance system, causing substantial productivity loss. To improve productivity of tunnel construction and enhance current practice of TBM guidance, this paper introduces research and prototyping of the Smart Laser Projector system. Unlike the traditional laser system, the Smart Laser Projector does not rely on maintaining the parallelism between the tunnel alignment and the laser beam. The system calculates the as-designed coordinates of the center in the same cross section of the as-designed tunnel model and projects a laser spot at the exact as-designed position of the center of the target board. It presents the as-designed position information to guide the operator just like the conventional laser, but its operations take much less effort of the surveyors, and the same level of accuracy and reliability can be maintained. A lab test is provided to demonstrate the concept and prototype.