Construction Research Congress 2022: Project Management and Delivery, Controls, and Design and Materials

Understanding how negotiations work between multiple parties has proven advantageous in strategizing through business decisions. However, precontract negotiations in the construction industry has not yet been exclusively studied in private design-build projects. Challenges and best practices associated with negotiating a design-build contract for a complex construction project are usually captured as tacit information through the experience of design-build professionals. To elicit this information from such experts, semi-structured interviews were conducted and analyzed in this study, aiming to explore current trends in precontract negotiations in design-build. Existing literature on the critical factors that significantly affect design-build contracts is utilized to build a scenario to prompt these interviews. These factors include payment terms, retentions, warranty/correction period, liquidated damages, and change orders. Moreover, the impact of scope definition and mutual trust between parties on the precontract negotiations were discussed. Ultimately, positions and trade-offs considered by the design-builder and the owner perspectives while negotiating a design-build contract in the private construction sector were presented. Some key findings for precontract negotiations in design-build from the interviews were (1) scope definition is not a decisive factor, (2) trust between contracting parties impacts the tone and speed of negotiations more than the content of the resultant contract, and (3) protecting subcontractor interests should be the priority for both the owner and design-builder. This study will be beneficial to design-build professionals to strategize through their precontract negotiations and maximize the performance of their design-build contracts. However, since this is an exploratory study, future investigation on presented findings is warranted.

Concurrent delay is a vexing problem at the intersection of construction scheduling and contract law that has long been studied to determine time extensions, compensable delays, and apportion liquidated damages among project participants. Nevertheless, there is no standard delay analysis method that fairly apportions such liabilities for activities that are parallel. This study presents a formal approach using game theory to transparently analyze and fairly apportion delays among stakeholders including owners, contractors, and especially subcontractors. Its four-steps employ the Shapley value, a cooperative game solution concept. Step 1 acquires as-planned and as-built schedules as input; step 2 classifies delayed activities; step 3 determines marginal contribution and probabilities; and step 4 calculates the Shapley value for each player to indicate its liability. Examples for two players and three players illustrate the detailed working of the new model. The contribution of this research is a comprehensive, fair, and simple solution for concurrent delays.

Construction joint ventures (CJVs) are often perceived as risky endeavors due to the differences in technical, cultural, and political backgrounds between the participating parties. Fair apportionment of risk costs is considered as one of the critical success factors of a CJV. This research aims to allocate CJV risk-contingency costs based on the formation risks attributed to each party. To this end, the authors developed a parametric framework that allocates potential CJV additional risk-contingency costs among parties via cooperative game theory (CGT). First, CJV formation risks are identified and assessed using an existing model from the literature that utilizes analytical hierarchy process and utility theory. Next, estimates for contingency costs required for risk mitigation are divided among partners using CGT. To demonstrate the proposed framework, an illustrative case study is presented. Results of the study highlight the utility of CGT in generating axiomatically fair shares of risk contingency costs that ultimately enhance the stability of the CJV.

State Departments of Transportation (DOTs) not only build highways and bridges, but also construct building projects for their sites, divisions, and head offices. All state DOTs have construction budgets every year for building new projects or renovating existing buildings. The project performances of highway and bridge projects built by state DOTs have been analyzed by numerous studies. There have been many studies conducted to determine project performance differences using different delivery methods by the same owners. However, no study has been conducted to determine the project performance difference in vertical and horizontal projects built by state highway agency using the Design-Bid-Build (DBB) method. Therefore, the researchers conducted this study to compare the performances of horizontal and vertical projects performed by the same state agency. Thus, this study collected the construction costs and schedule data of vertical and horizontal projects built by Massachusetts Turnpike Authority (MTA), in order to determine whether the performance of vertical projects is different from horizontal projects. The authors used cost growth and construction intensity as the metrics to measure project performance of horizontal and vertical projects. The study findings show that the cost growth and construction intensity of vertical projects are better than horizontal projects; however, the differences were not significant. This showed that MTA’s personnel are managing both types of projects with similar efficiency and productivity. It is recommended to conduct similar study with other state DOT’s data, so that it can be determined whether other state DOTs are managing these two types of projects with equal efficiency.

The utility coordination is part of project development and delivery and can be fundamentally influenced by the contracting method. Effective utility coordination can improve the delivery of transportation and other capital facility projects and reduce project risks posed by delays, safety hazards, and cost overruns. State Transportation Agencies (STAs) are implementing many strategies to address road conditions rapidly. One approach has been using alternative contracting methods (ACMs) to speed project delivery, improve quality and constructability, or manage other resource constraints. This paper investigates the efficiency, safety, cost, and schedule impacts of ACMs on utility coordination compared to traditionally delivered projects through design bid build (DBB). Understanding these impacts may influence the selection of a project delivery method or the utility coordination approach used. A literature review, survey, and interviews with subject matter experts were performed in order to achieve this objective. In summary, differences between utility coordination on ACM and DBB projects were perceived. Furthermore, utility coordination costs can be higher on projects using ACMs when compared to DBB. Challenges were encountered in the availability of documentation regatding utility coordination impacts on safety, and schedule STAs are encouraged to seek approaches to collect this data.

The COVID-19 pandemic has triggered unprecedented political, social, and economic repercussions that simultaneously affect the public, private, and users, triggering construction, demand, and credit risks in road Public-Private Partnerships (PPP). Given the magnitude of this impact, incipient literature has analyzed how specific projects addressed the shock from multiple perspectives in record time. Nevertheless, a thorough comparison of contractual renegotiations produced by the pandemic among initiation processes [i.e., Solicited Proposals (SP) and Unsolicited Proposals (USP)] is missing. Therefore, this paper aims to extend the understanding of COVID implications by analyzing PPP contractual governance, under unprecedented uncertainty, in the most recent road PPP program in Colombia, including 29 projects. This analysis was developed with a case study based on public information. Findings reveal that contractual modifications derived from COVID-19 were mainly driven by extending the project schedule, increasing concessionaire’s liquidity in the middle term, and reducing the project scope. Overall, the empirical findings indicate that USP was much more renegotiated than SP, which suggests that the public sector is more prone to guaranteeing the intended private’s sector conditions in projects initiated by the same private sector.

The COVID-19 pandemic has increased contractual concerns under contingencies for public–private partnership (P3) projects. Conventional manual contract extraction is time-consuming and error-prone. Devising a method for automatic contract extraction can support contract management in this aspect. This research proposes a rule-based natural language processing (NLP) approach to extracting contingency liabilities allocated between the public sector and the private sector in the contract. The model consists of a domain-specific lexicon developed based on 21 US transportation P3 concession agreements and a set of matching rules to identify target sentences which fall into five classes, namely remedy entitlement, remedy obligation, liability waiver, mitigation, and termination. This automatic process can reduce the time and cost of the contract review process and help identify issues that the contracting parties should consider going forward in drafting new contracts or in amending existing contracts to avoid potential disputes, in response to consequences of contingencies, including the COVID-19 pandemic.

Information management (IM) is a critical component to projects in the architecture, engineering, and construction industry. A defined IM process can improve a project’s lifecycle, from project delivery to operations and maintenance. Typically, owners are the project stakeholders that can benefit the most from having a defined IM process as having current, relevant, and accurate information can improve decision-making and overall project performance. To this end, a longitudinal study of university construction contract documents with respect to IM was carried out. The focus was on universities as they are owners and operators who have a vested interest in their project’s lifecycle. Researchers evaluated contract documents comprised of the construction manager at risk (CMAR) agreement and the general conditions document for one university system and these documents span 2002–2019. Three versions of the CMAR agreement and three versions of the general conditions documents were analyzed and compared using qualitative analysis and coding. The main areas of analysis were information transactions, IM systems, and legal factors impacting IM. Results of this analysis show that as new IM processes, such as building information modeling, were adopted in the organization, more specific language regarding the process of IM were included in the contract documents. These findings illustrate how IM has evolved over time in university contracts between owners and contractors.

An increasing trend is observed in transportation agencies’ use of the Public–Private Partnership (P3) delivery method, which is attributed to its many cited advantages, such as private financing and transfer of operations and maintenance risks to the concessionaire. As more P3 projects are being implemented nationwide, the question is posed on how effective the contracts have been drafted to account for such risks. The objective of this paper is, thus, to investigate how P3 contracts are drafted to allocate risks between the owner and concessionaire, identify the major causes of claims and disputes on these projects, and accordingly provide recommendations on how contracts could be effectively drafted to mitigate such risks. Results show that when owners take responsibility of the significant projects’ risks, such as utility adjustments and third-party approvals, projects are likely to experience fewer claims than other projects where the owner passes all the risks to the concessionaire.

Early contractor involvement in Accelerated Bridge Construction (ABC) projects has several benefits including cost savings, reduced traffic disturbance as well as construction time and nurturing the adoption of innovation methods in construction. Alternative Technical Concepts (ATC) is one method of early contractor involvement to propose modifications to contract requirements before the bidding or proposal process. This study investigates the ABC projects’ performance utilizing ATCs in contract delivery by conducting interviews to project stakeholders involved with most of the ABC projects in the US that integrated ATCs. The obtained data are analyzed to develop: (1) a framework of streamline procedure for integration of ATCs in different ABC projects; and (2) a regression model demonstrating the factors impacting cost savings in ATC-based ABC projects. The results indicated that the proposed framework would facilitate stakeholders in decision-making for ATC adoption in ABC projects. The regression model showed that ATCs approved with conditions positively influence cost savings in ABC projects.

The aftermath of the COVID-19 pandemic is triggering an extraordinary increase in governments’ fiscal pressure and debt as public expenditure increases and taxes decrease simultaneously. Given that annual payment to road Public–Private Partnerships (PPP) is one of the several significant burdens on government budgets, multiple PPP renegotiations are likely to tackle revenue underperformance due to traffic or toll tariff reductions produced in the next few years by the aftermaths of the pandemic. The current study develops a flight simulator relying on a System Dynamics (SD) model to improve the understanding of decision-makers about the complex relationship between project sources (i.e., revenues and government’s subsidies) and uses [i.e., capital expenditure (CAPEX), operational expenditures (OPEX), and financial costs] through the concessionaire’s perspective. The proposed simulator helps increase the understanding of decision-makers about suitable alternatives for balancing revenues underperformance in the operation phase without increasing public subsidies. Furthermore, this simulator allows exploring policies by renegotiating the debt conditions or the OPEX for guaranteeing the life-cycle financial sustainability of PPPs. Results demonstrate that extending the debt’s repayment period should be the preferred renegotiation alternative for balancing significant revenue underperformance. This time extension must occur after the construction phase is finished because it must not affect the project service levels, public subsidies, or bank’s rate of return.

Selection and implementation of project delivery methods are important decisions in an airport capital project that will determine how the airport agency employs procurement and financing strategies, management structures, and cash flows. Most current delivery method selection approaches in the aviation sector rely on experience and opinions of construction experts. A research gap exists regarding understandings of existing and emerging delivery methods as well as their alignment with procurement and management structures, organizational capabilities, and financial planning considerations. The objective of this study was to investigate the current state of practice in selection and implementation of project delivery methods in airport projects. A content analysis of 26 airport projects across the US was performed to synthesize documents regarding selection and implementation of project delivery methods and procurement approaches. The results show that alternative contracting methods, including construction manager-at-risk, design-build, and public–private partnership, are increasingly used in authority/quasi-government airports because of their connection to financial planning, contract strategies, and procurement methods. This study contributes to the body of knowledge by providing a comprehensive review of current practices of selecting and implementing project delivery methods in capital airport projects. The results also imply the importance of considering risk-based methodologies for project delivery selection in the aviation sector.

Innovation is an important driver for project delivery method (PDM) selection on infrastructure projects. However, the literature review indicated a gap in explicitly defining the term in the context of PDM selection for transportation projects. This study employed data from 20 interviews with subject matter experts (SMEs) and found that innovation can be defined through two major dimensions: project characteristics and innovation opportunities. Project characteristics include the inherent project elements that increase complexity and can be categorized into traffic challenges, design elements, and project constraints. Innovation opportunities relate to exogenous elements where constructors can apply innovative solutions; they include traffic handling, developers’ resource and schedule optimization, and design and construction methods. An expert panel from the Texas Department of Transportation validated the defined innovation dimensions and their use in the PDM selection process. The experts qualitatively evaluated the innovation and complexity levels of 32 projects and found that traffic challenges most often contribute to increased project complexity.

When ensuring the growth and sustainability of infrastructure projects, a significant amount of capital must be available. For this reason, the public sector needs to seek new financing methods, and the private sector is willing to contribute to this financial muscle through local and foreign Equity Provider participation. Nevertheless, it is necessary to outline the risk accurately to guarantee private participation. Furthermore, many scholars have shown that foreign Equity Providers’ (EP) participation can bring new ideas and expertise to develop local markets. The EPs seek to mitigate financial risks by collaborative ventures. The influence, holding, and participation of foreign and local EPs in local industries can be understood through Cross-Holding Matrixes (CHM) and visualized using network diagrams. This research uses CHM to understand the interdependence between infrastructure investments in some European countries (Germany, France, Netherlands, Italy, the UK, Spain, Austria, Portugal, and Belgium). The results show that Spain, France, and the UK position themselves within the road infrastructure projects, while the latter appears as the leader for the social infrastructure sector.

Public–Private Partnerships (PPP) provide an avenue to deliver transportation infrastructure projects when public funding is limited. The financial mechanism is a significant component of PPP projects due to the infusion of private investment into public projects. The literature on PPP and its development is substantial, whereas funding and financial assessment has received less attention and examination. The research presented in this paper was an investigation of the financial risks in PPP projects through a literature review, survey, and follow-up interviews. Financial risks associated with PPP projects include inappropriate financial structure, tedious determination of private entity creditworthiness, high-procurement costs, operation and maintenance costs inaccurately estimated, risk of capital renewal cost due to inaccurate estimates, incorrectly predicting key performance indicators, inability to sustain a revenue stream during the concession period, intervention of the public entity in cost predictions, and inefficient revenue risk sharing mechanisms. The practical application of this study is valuable to both contractors considering PPP investment, and owners/public entities mitigating risk associated with contractor tendering. By identifying these risks, construction companies are able to anticipate financial risks for PPP projects to strategize remedial actions.

Scholars have long acknowledged the impact of institutional distance on the level of control in the governance structure of PPPs, but there still have ambiguity in their driving mechanisms, leading to contradictory results. Through 10,945 decisions of the level of control by private sponsors in governance structures from 120 countries between 1996 and 2019, we explore the effects of formal and informal distance as well as the moderating roles of the host country’s formal institution and home country’s informal institution to the level of control by private sponsors. Our findings denote that the higher formal distance encourages the private sponsors to choose a higher level of control so as to except their competitive advantages to the new markets, while the informal distance has an inverted U-shaped relationship with the vertex at 11.5 for the “optimal distance” for maximizing control. Besides, the formal institution of host countries intensifies the impact of formal distance. This study enriched PPP literature with diverse mechanisms beyond the formal and informal distance and addressed their boundary conditions in formal institutions of host countries.

Claims and legal disputes in construction projects largely result in cost overruns, delays, and adversarial working relationships among contracting parties. Most disputes in construction projects are caused by the inadequate drafting, review, and management of legal documents such as contracts, standards, and codes. In this regard, Natural language processing (NLP) offers a collection of methods that can be employed to process the complex text of legal documents to prevent the root causes of disputes. Although several researchers have investigated the role of NLP in addressing legal issues, this line of research is still at a very early stage. The current study presents a comprehensive review of available literature on the exploration of NLP in preventing several common legal issues associated with drafting, reviewing, and managing different legal documents. Identifying and documenting previously developed NLP-based frameworks, their effectiveness, and limitations could help researchers get critical information to further explore NLP in minimizing legal issues.

The constant pressure to deliver emergency projects (e.g., emergency rebuild due to natural disasters) and non-emergency projects (e.g., first to market) at a faster rate requires flash tracking, delivery approach characterized by a heightened concurrency between the project phases and the involvement of the key upstream and downstream stakeholders early in the planning and design process. To enable flash tracking, the construction-driven design has evolved as a program delivery process that integrates the building approach with the design process where the contractor has the primary authority over the design approach. In addition to the contractor, the involvement of key project stakeholders such as major subcontractors, major equipment vendors, suppliers, and operations and maintenance personnel, early in the design process becomes essential for the construction-driven design and overall project success. Large domestic and global construction and design firms have been using this approach to facilitate faster project delivery and achieve cost savings. Through use of qualitative research methodologies, this paper defines construction-driven designs, describes its characteristics, and identifies the steps required for successful development and execution of the construction-driven-designs. It assesses the preferred timing and the level of involvement of the key stakeholders in the design and discusses stakeholder experience requirements. Additionally, this paper discusses recent project successes using construction-driven approach such as Tappan-Zee Bridge and St. Anthony Falls Bridge. Ultimately, this research will provide guidelines to owners and contractors wishing to use the construction-driven approach to deliver projects at a faster rate.

As innovative project delivery methods, such as design-build (DB) and public–private partnership (P3), become more common, many state departments of transportation (state DOTs) have implemented alternative quality assurance programs (QAPs). These programs shift quality assurance responsibility from the DOT to the private sector in DB projects, but this transition can be a challenge, creating a gap between DOT expectations and the construction industry’s actual implementation. It is crucial to ensure that DOTs have clear requirements defined for quality assurance programs for innovative delivery projects. It is also critical for the construction engineering and inspection (CEI) industry to understand DOT’s quality assurance expectations in the innovative delivery environment. The major objectives of this research are to identify the state of practice in QAPs and characterize the variability among state DOTs’ DB and P3 projects. This study conducts content analysis and comparative analysis of QAPs across the population of US DOTs. This involves comparatively examining 35 DOTs’ QAPs and analyzing 98 documents such as requests for qualifications, requests for proposals, and quality management manuals to understand different DOT’s expectations for DB and P3 projects. The study identifies five essential criteria representing each DOT’s unique quality assurance requirements and compiles primary similarities and differences in QAPs across the DOTs. This research contributes to the body of knowledge on the core criteria of quality assurance requirements and DOTs’ expectations specified in DB and P3 contract documents. The findings will help practitioners understand alternative QAPs better and increase the quality of performance.

Construction manager at risk (CMAR) emerged as an alternative project delivery method for efficient delivery of construction projects. In CMAR, the design is not completed when the contractor is selected; therefore, a price-focused contractor selection based on the lowest bid is often not appropriate. Instead, qualifications-driven processes are used. The objective of this paper is to identify the current practices of multi-stepped qualifications-based selection (QBS) processes while also uncovering the major qualification criteria used for CMAR projects. A total of 33 CMAR requests for qualifications (RFQs) and request for proposals (RFPs) were collected from 30 states. Patterns were discovered in the selection processes along with qualification criteria, prioritizing firm experience, key personnel, past performance, and the contractor’s approach to the project, which all rose to the top of the criteria list for CMAR projects. This research supports project owners developing RFQs and RFPs by informing a successful CMAR selection process.

Best-value procurement enables highway agencies to make informed selections by assessing not only cost but also technical criteria. However, assessing technical criteria is necessary but not sufficient to reach a best-value selection that is not biased toward cost. In the last two decades, the ranges of scores used in the assessment of technical proposals have shown to play an influential role in the bias toward cost of best-value selections. This study investigates whether there is any significant difference between technical scores assigned to design-builders in best-value procurements using different award algorithms. The research used statistical and goodness-of-fit analysis to characterize, evaluate, and compare 822 technical scores from 240 design-build highway projects procured using best-value procurement in the US. The study showed that technical scoring trends are statistically significantly different across weighted criteria, adjusted bid, and adjusted score algorithms. Results suggest that the weighted criteria algorithm facilitates a better differentiation among proposers based on technical criteria, resulting in a higher dispersion of technical scores than adjusted bid and adjusted score algorithms. Highway agencies may consider this research’s results as a first steppingstone to refining the processes that serve to define and select best-value core elements such as the award algorithms.

Properly managing geotechnical risks early on within construction projects is crucial in preventing claims and disputes over differing site conditions (DSCs). This becomes even more important as projects are continuing to increase in size and complexity. In addition, when design-build (DB) project delivery method is used on such large projects, there is limited time available to investigate geotechnical risks, and comprehensive geotechnical studies are relegated to the post-award timeframe. This study, investigates how geotechnical risks are being managed and how contracts allocate those risks on mega transportation projects, and provides recommendations on how to best manage such risks, given the increased size and complexity of these DB projects. This is achieved by following the risk management process: (1) identifying and managing geotechnical risks on mega transportation projects through the use of formal risk management programs and risk registers, and (2) allocating risks between the owner and the contractor through the DSC clause. This approach will ultimately aid owners in making decisions on how to effectively manage and allocate risks on mega DB infrastructure projects.

This study investigates the influence of the uncertainty factors in the pre-bid clarification document on bid price formation during the project bidding. To this end, civil projects from the California Department of Transportation were used as research data. This study randomly selected 50 sample data from each of 12 counties in 2008–2020: a total of 600. Within the data, this study examined the project uncertainty cases. Based on the analysis, four uncertainty factors: ‘conflict’ (UF1), ‘impossibility’ (UF2), ‘lack’ (UF3), and ‘missing’ (UF4) were extracted. With the project not containing any uncertainty factors as a control group, the project containing these uncertainty factors was designated as an experimental group. After comparing the bidder’s price, the experimental group’s bid price was formed at a higher level than the control group. This result suggests that uncertainty factors in bid documents induce bidders to set a high-bid price as a defense against uncertainty.

Design-Build (DB) is a popular alternative project delivery (APD) method to be used in place of the traditional Design-Bid-Build method for construction projects. In the Southeastern United States, each state’s application of DB projects is nuanced according to the legal language that constructs the foundation of its usage in contracts. The goal of this paper is to examine a possible connection between legislative involvement over a span of time and consistent implementation of DB in transportation projects. For the scope of this study, historical DB project data on Department of Transportation (DOT) projects from the state of Georgia and its neighboring states including Alabama, Florida, North Carolina, South Carolina, and Tennessee were used. This research will serve to aid states in the Southeastern region of the US seeking to implement DB delivery in DOT projects by providing analysis of comparable paths of legislation. The target audience comprises many disciplines from academia, engineering, professionals, to legislators.

With some of the highest population and economic growth rates globally, five developing countries in Southeast Asia—Vietnam, Thailand, the Philippines, Malaysia, and Indonesia—face urgent transportation infrastructure needs. Decreasing access to traditional sources of financial aid and increasing budgetary deficits in these countries have reduced the capacity of conventional approaches for infrastructure delivery. Consequently, their governments are promoting public–private partnerships (PPPs) as one solution to address these needs, but they are still struggling with many challenges. While the literature has identified some country-specific challenges, a comprehensive synthesis of common and unique challenges among the countries is still lacking. This investigation addresses this shortcoming by identifying 40 challenges found in 33 sources in both academic and non-academic literature based on the frequency of occurrence. The major challenges identified include: legal and policy issues, effective procurement processes, and financial market access. This synthesis establishes what key issues need remedying in these Southeast Asian countries before PPP implementation can be effective.

One of the early owners’ decisions that have significant impact on project success is selecting a suitable project delivery method. One of emerging project delivery method that gaining high interest in the past few years is Progressive Design Build (PDB). However, limited knowledge is available regarding circumstances under which one should select PDB as a suitable delivery method for a project. Therefore, this study determined the relative importance of the variables that impact owners’ decision to select PDB as a delivery method for a project. To achieve this goal, the research team developed and distributed an online questionnaire among project managers who were involved in PDB projects, and responses to it were collected to measure the importance of factors that affect owners’ decisions to select PDB as a suitable delivery method. Within academia, this study establishes an early empirical database of the PDB selection factors. Within industry, this study enhances the decision-making process by providing guidelines for using PDB as a delivery method based on owner and project characteristics.

Construction is one of the major contributors to the US economy. Due to the existing interdependencies between industries, the loss of production in one industry influences the production of other industries which are dependent on it. This makes the construction industry vulnerable to the inoperability induced by other producing sectors like manufacturing, transportation, etc. This paper presents the interdependencies between all the constituting sectors of the US economy to quantify the economic vulnerability of the construction industry. To achieve the said objective, the paper has adopted Input-output Model using Make and Use tables from the US Bureau of Economic Analysis (BEA). The outcome of this paper is the comparison of the influence of different sectors’ production inoperability on construction industry. This outcome can be used by the decision makers to identify the industries which have the potential to affect construction industry’s production thus making it resilient to the exogenous shocks in other producing sectors.

Current construction projects often suffer from a lack of synchronization between on-site material requirements and supply. This is at least partially because of the use of a fixed rolling planning period and the length of material lead times. The length of the rolling planning period matters because it affects when materials are ordered. The later the materials are ordered, the higher the chance of having material shortages that will cause progress delays. The earlier the materials are ordered, the higher the chance of having them delivered too early and having to organize storage and keeping track of inventory. This study proposes a methodology to determine the optimal rolling planning period for construction projects, that is, the period that provides the lowest total cost considering unexpected delays in construction progress and the unexpected need to store materials on site. The optimal period was determined using data extracted from a regularly updated building information model (BIM) and a heuristic search algorithm. The methodology is used to plan the raw materials for site-mix concrete for an office building project to be completed in four weeks. It is shown that the methodology can reduce costs related to materials arriving too early or too late on site.

Accurate insurance cost estimation is crucial for contractors. This paper presents a machine-learning decision support tool to estimate optimal insurance premiums for multiple attempted bids. The authors adopted a three-step methodology: (1) construct a Past Project Insurance Database (PPID) including estimated versus post-award premiums; (2) create an Insurance Providers Database (IPD) including insurer attributes and evaluations; and (3) develop a Neural Network Selection Engine (NNSE) for premium estimation. To demonstrate the model, a Class A general contractor in Egypt is presented, featuring 40 of its past projects and 22 insurers, while considering 12 attempted bids. Using the Multilayer Perception (MLP) back-propagation algorithm, the NNSE achieved a training error of 2.2%. Additional validation was performed using the J48 Decision Tree Classifier algorithm, achieving an error of 0.99%. Further validation is recommended to compare actual policy premiums with the results predicted by the model. This paper contributes to the body of knowledge by presenting a machine learning approach for contractors in developing countries to optimize their construction insurance profiles.

Forecasting the level of competition is critical for letting, investing, and pricing strategies for transportation owners and contractors. For instance, it helps owner organizations establish better letting strategies, such as scheduling projects for bid, balancing projects in a letting, and packaging projects into proposals. Moreover, for the contractors, it helps to establish investing and pricing strategies, such as the decision for participating in a bid and adjusting the mark-up (profit) level for the bid. Thus, this paper aims to identify the leading indicators of the level of competition for transportation projects and create multivariate time series models for forecasting the level of competition. The study uses the monthly level of competition of highway projects let in the state of Georgia between 2010 and 2018. Ten potential leading indicators of the level of competition, representing the local highway construction market, the construction market, the energy market, and macroeconomic market conditions, were used in this study. This study found the gross domestic product (GDP) of the Georgia construction industry and the number of hires as the leading indicators of the level of competition through the Granger causality test. Vector Error Correction (VEC) models were then developed for forecasting the level of competition with the identified leading indicators. The predictability of the developed VEC model for the level of competition represents a useful tool for making decisions in letting, investing, and pricing the transportation project.

Despite numerous attempts toward enhancing performance, the construction industry is still behind in this term. Various technological advancements are at the disposal of construction researchers and practitioners to address this issue. Machine learning techniques are one example of technologies that have become readily accessible to general users thanks to the efforts of researchers in construction planning and control and other fields. Accordingly, the performance in the construction industry may be improved by employing machine learning techniques for developing performance indicators to forecast possible issues or take corrective measures proactively. While some studies have applied machine learning in various aspects of lean construction, no research has yet employed machine learning to predict specific performance metrics. This study, therefore, aims at developing a framework to predict the Last Planner System (LPS) metrics. This will allow optimizing performance on a near real-time basis. A framework for implementing such an approach toward project control is presented, and several predictive models are created, compared, and refined. This approach opens an entirely new avenue for applications of machine learning and data mining techniques in lean construction project planning and control.

An accurate cash flow model is essential because it helps state highway agencies forecast the funding obligations, reduce the financing burden, and streamline the delivery of their highway projects. Thus, the primary objective of this study is to discover the pattern of expenditure curves of highway projects and empirically explore the associations between affective factors and patterns of highway project expenditure curves. This paper analyzed the cash flows of 554 completed highway projects let in the State of Georgia. This study identifies five cash flow clusters using the unsupervised k-means clustering and analyzes the patterns of construction expenditure project cash flow with project-related attributes using multinomial logistic regression. Seven attributes, including owner estimate, number of pay items, and types of pay items, have statistically significant impacts on the shape of expenditure cash flow of highway projects. This study contributes to preparing more accurate estimates on expenditure cash flow for a project, making better letting/bidding decisions, and managing finance more effectively.

Construction projects encounter numerous risk factors during their life cycle. Some risk factors can impede the progress of all or some activities and cause their durations to be correlated, which ultimately affects the entire schedule. This is especially visible in linear and repetitive schedules that plot the detailed work progress over time. This paper explores such risk factors that cause correlated activities and investigates their influences on fuzzy linear schedules. The contribution is twofold: First, it categorizes risk factors into global and local according to whether their effect is upon all activities within the schedule or not. It creates a mathematical model to simulate such risk factors on fuzzy linear schedules to evaluate and visualize their effects on project durations. Second, it investigates the promote-or-inhibit effect based on the correlation values that they can cause (where 0 to +1 promotes and −1 to 0 inhibits) within various schedules. This research enables decision-makers to evaluate risk factors to protect schedules by taking preventive action.

Reasonable schedule management can enable the project to complete tasks on time with minimum resources, including time and labor force. Although schedule management has been greatly enhanced by BIM technology, some dispensable processes still pose negative influences on schedule management. To ameliorate it, this paper proposed a framework of an auto-updated and operational construction schedule management system. It contains four components, which are schedule tasks decomposition, daily task assignment, daily task conducting, and data submission, as well as the operation of Central Data Processing Service Platform (CDPSP). Besides, a program simulation of site assistance is conducted in this paper as a key element. By analyzing practical values, it can be found that the proposed automatic system offers significant benefits in reducing dispensable processes and optimizing schedule management. Further implementation of the system could assist various projects participants in real-time schedule monitoring, resources allocation for multiple projects, and deepening the application of cloud-sharing and cloud-cooperation.

Nowadays, construction schedule generation still mostly relies on manual efforts to arrange activity information and fulfill the scheduling tasks. There is a lack of automation and efficiency. The heavy reliance on manual efforts may also introduce human errors in the process. Although there are many construction scheduling software platforms, such as Synchro, Primavera 6, and Powerproject, they require manual input of activity information based on procedural documents or experience. To bridge the gap of automation in generating scheduling information, in this paper, the authors proposed a semantic natural language processing (NLP)-based information extraction (IE) method to automatically extract, process, and analyze procedural information from procedural documents to support construction scheduling applications. This method automatically generates a report of all activities in a right sequence considering each activity’s predecessors and successors, which can reduce manual efforts in arranging activity information for scheduling tasks. An experiment was conducted on a set of open-source specifications to demonstrate the performance of procedural information processing when using the proposed IE method. Comparing to a manually developed gold standard, 95.83% precision and 90.45% recall were achieved using the proposed IE method for the extraction of construction procedural information. In addition, running the IE algorithm to generate activity information led to 89.33% time saving when compared to manual activity information generation. The high performance achieved in accuracy and efficiency using the proposed method showed it is promising in bringing the traditional manual construction scheduling process one step closer to full automation.

State Departments of Transportation (state DOTs) are constantly challenged by accurate estimate cost of highway projects. The significant deviation between the lowest submitted bids and the owner’s estimate is problematic for state DOTs that strive to deliver projects on time and within budget. However, few empirical studies focused on narrowing the gap between low bids and owner’s estimate. The major objective of this research is to enhance the quality of owner’s estimate to more accurately forecast the lowest submitted bid. To achieve this objective, the feedforward neural network in the framework of Friedman’s model is utilized to enhance owner’s estimate. A pool of potential variables to explain the cost variability are selected as input attributes, representing bidding environment, construction market, macro-economic market, and energy market conditions. Bid tabulations for highway construction projects let by the Georgia Department of Transportation (GDOT) from 2010 to 2018 are utilized to conduct empirical study and quantitatively measure the model performance. The outcome shows that the proposed model significantly reduces the deviation between owner’s estimate and the submitted low bid. This study contributes to the body of knowledge through creation of an artificial neural network model that improves the accuracy of owner’s estimate and reduces the gap between owner’s estimate and the lowest submitted bid. It is anticipated that transportation professionals benefit from the results of this study to prepare more accurate cost estimates and budget plans for enhanced decision-making.

Project planning and controls requires planners to continuously revise project schedules to meet evolving requirements and constraints during a construction. Such an activity is usually performed under strict deadlines and planners are often forced to set aside good planning principles to deliver the updated schedules on time. To assist planners with validating their schedules, this paper explores the feasibility of using an integrated approach based on heuristics and machine learning methods to check the quality of a construction schedule. Specifically, building on the predefined rules and heuristics formulated in the Defense Contract Management Agency (DCMA)’s 14 Point Schedule Quality Assessment, this paper explores the feasibility of heuristic-based and deep learning methods to assess a project schedule health from qualitative and quantitative perspectives. Experimental results from thirty-five real-world projects are presented which demonstrate the feasibility of these underlying methods in highlighting schedule deviations from industry guidelines as well as following the best planning practices. A path forward toward a completely automated schedule health assessment system is discussed in detail.

The Covid-19 pandemic has affected industries globally: many have regressed due to stoppages, others have flourished, while some have remained dormant. The construction industry’s nature as “labor intensive” has forced some governments to allow its continuation through lockdowns and unstable times. Overall economic conditions can be explained through macroeconomic indicators as CPI, PPI, stock market indices, and unemployment rate. Construction industry’s output can be interpreted from indicators as the country’s GDP from construction. In Egypt, while Covid-19 impact has hit the economy, the overall GDP growth rate has dropped for the first time in nine years from 5% to –1.7%, while the GDP from construction has not been significantly affected presenting high resiliency. This research aims to propose a neural networks model to predict construction industry’s resilience, explained through GDP from construction, using lagged macroeconomic indicators as predictors. Granger causality test is utilized to identify which macroeconomic indicators can be the leading indicators of the GDP from construction up to three quarters ahead. Data in Egypt from June 2008 to December 2020 are used as the study domain. Results show that although long term correlation exists, construction industry in Egypt can be classified as a resilient one. Such an approach can be applied on global markets providing the ability to predict resilience of an industry during economic shocks and unstable times, which would better prepare governments, investors, and shareholders.

Accurately modeling productivity is essential for ensuring that the results of construction process simulation models align with actual practice. Since collecting quantitative data is challenging and expensive, productivity models are often built using the information provided by industry experts. The subjectivity of this information, however, commonly results in oversimplified or inadequate productivity models. To address this challenge, this research has developed a novel framework that reduces the subjectivity associated with labor productivity modeling by identifying interrelationships between factors affecting productivity that individual subject experts may have overlooked. A Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to identify relationships (i.e., dependencies) between factors, which is integrated with an Analytic Network Process (ANP)-based approach to determine the strength (i.e., weight) of each relationship. Results can support decision-making or feed productivity data to simulation, empirical, or dynamic models of construction systems. Outputs of the proposed method yield higher-quality inputs for productivity modeling-based decision-support systems compared to traditional input preparation approaches. The effectiveness of the framework is demonstrated through an illustrative example.

The US Army Corps of Engineers Civil Works projects are designed to provide national management of water resources. These works use emergency streambank protection to protect public facilities, like bridges and highways. The projects under this authority have limited funding, therefore, many design and field studies are omitted, which increases project uncertainties resulting in cost overruns and contract modifications. This study analyzes previously constructed projects by comparing the independent government estimate with the contractor’s actual costs and identifying the variables causing these differences via statistical analysis. The variables identified are categorized into those elements that affect the accuracy of the government estimate. From the findings of this categorization, an uncertainty analysis is utilized in order to quantify the results. These results serve as a basis for future government estimates and projects of this category to select the best proposal and minimize the number of change orders and the risk of cost overruns.

The construction market presents volatile fluctuations through time. These fluctuations are problematic for project planners and decision-makers to accurately assess business conditions and develop business outlooks. Therefore, it is necessary to forecast the market trends and fluctuations as accurately as possible for planning construction projects, allocating resources, and formulating strategies. Practitioners refer to Architecture Billings Index (ABI) as a leading indicator in the construction market. ABI forecasting can assist project planners in tracking construction market fluctuations to properly allocate resources and identify business opportunities. The main objective of this research is to develop time series models for forecasting ABI. The historical ABI data are used to develop the univariate time series models and validate their forecasting performance. The results show that the seasonal autoregressive integrated moving average (Seasonal ARIMA) model provides the most accurate out-of-sample forecasts. The findings of this research can assist project planners and decision-makers in forecasting volatile fluctuations in the construction market for project planning and strategic decision-making.

The standard specifications of state Departments of Transportation (DOTs) serve as the basis for project schedule control, establishing minimum requirements for the contractor schedule quality and allowing DOTs to effectively communicate and manage project schedule performance. However, the level of detail and sophistication varies across DOTs, and few references are available to guide DOTs in improving their standard specifications for contractor schedule management. This paper summarizes the current status of the contractor schedule requirements stipulated in the standard specifications for road construction projects at 18 DOTs. Key schedule requirements were extracted and categorized under four high-level criteria and 14 sub-criteria developed by reviewing past literature and the DOTs’ schedule specifications. The specification items across the 18 DOTs were then compared to identify any gaps between the various states’ requirements. The outcome of this study will help state DOTs assess the current status of their contractor schedule requirements and identify areas for improvement, which will enhance schedule management and contractor schedule quality. It will also contribute to academia by providing insight into the current schedule management practices of DOTs for road construction projects.

Activity sequence determination plays a crucial role in construction scheduling. Many studies have been conducted to leverage sequence knowledge from historical schedules. However, they still lack in fully capturing the sequence knowledge due to the inherent unstructured characteristics of sequential patterns. This research applies an advanced Machine Learning technique called Long Short-Term Memory Recurrent Neural Network on historical schedule data to learn the activity dependency and sequence logic. The study collected 311 as-built highway schedules from a state department of transportation. Word Embedding approach is used for semantic annotation of activities to map them to feature vectors. Features based on work type and resource utilization are assigned to each activity. The model is trained to take a sequence of schedules and predict the most likely successors. The model validation yielded the accuracy of 80%. The activity feature vectors are used to visualize the project schedules. The contributions of this paper are (1) the visualized information of project activities can significantly help schedulers easily identify similar projects, and (2) schedulers can use the successor activity prediction capability of the model in activity sequence determination in developing pre-construction schedules and during construction update schedules.

Building operation and maintenance costs are reported to be the longest and most costly phase of a building’s lifecycle and it exceeds the total cost of initial design and construction. The present study focuses on developing a new model for optimizing the upgrade and maintenance interventions for existing commercial and residential buildings to minimize their operation and maintenance costs in a predefined period of study while complying with specified annual budgets and building operational performance constraints. The optimization model is developed in three stages: (1) identifying model decision variables, formulating objective function, and constraints; (2) implementing model computations using binary linear programming; and (3) analyzing the performance of the optimization model using a case study. To ensure that the model generates practical solutions, it integrated a set of constraints to comply with annual budgets, components’ service lives, and expected operational performance. The results of the case study analysis showed that the developed model was able to identify the minimum building operation and maintenance costs associated with various annual operational budgets that ranged from $165K to $300K and for a study period of 20 years. The model is designed to generate an action report of maintenance and upgrade interventions that include detailed recommendations on the building components and equipment repair, replacement, and upgrade for each year in the study period.

Estimating electricity consumption of buildings is an essential task in building energy management to enable decision makers to compare different energy efficiency measures and control strategies. With the growth in available building energy data, many opportunities have been emerged to apply Machine Learning (ML) methods for prediction of building energy performance. The objective of this paper is to (1) analyze and evaluate the influence of different building features on electricity consumption, and (2) evaluate the predictive performance of different ML methods in estimating electricity consumption of buildings with various occupancy types, surface area, locations, and climates. To this end, feature importance assessment is performed using Mutual Information (MI) from information theory. Moreover, four frequently used ML methods, including decision tree, random forest, multilayer perceptron, and gradient boosting are implemented to predict building electricity consumption using the latest version of Commercial Buildings Energy Consumption Survey (CBECS) data. The primary contributions that this research adds to the body of knowledge are (1) the application of information theory to evaluate the influence of different building features on electricity consumption, and (2) the development of new ML models to predict electricity consumption of buildings with different characteristics such as occupancy, surface area, location, and climate. Based on MI analysis square footage, number of employees, cooling equipment type, and principal building activity had the highest influence on building total electricity consumption. Moreover, random forest had the best performance with the coefficient of determination of 0.79 among other ML methods.

This study aims to develop a discrete event simulation model for improving simultaneous rebar manufacturing efficiency for multiple projects and each site. To this end, the simulation model was verified by comparing the production process and the actual rebar manufacturing plant’s processing volume. Then, various alternatives for establishing an optimal production plan by identifying the loss rate and the time required to process are analyzed. When the rebar was processed by combining the two sites and setting the priority in the whole reinforcing steel processing process, the lowest loss rate occurred because the type of loss length was small. Although the ratio of raw rebar by length is less influential than the combination by site, the ratio of 2:1 of 8 m and 10 m of rebar is considered a way to optimize the production plan.

The current practice of labor allocation in construction assumes single-skilled workforce; meaning that each worker is assumed to be skilled in one trade. In such practice, at any instance in the project lifecycle, some of the activities may be delayed due to the unavailability of a certain type of workforce that is engaged in other activities. Due to the inefficiencies of these solutions, some research discussed the idea of “multi-skilled” labor, where some of the workers may have enough training to carry out different activity types. Yet, the literature falls short in providing a robust multi-skilling framework; specifically, one that solves the problem of partial allocation of labor. Unlike all the previous multi-skilling research, the implementation of partial allocation enables secondary resources to be deployed at any day of the activity’s duration as opposed to only allocating resources that are idle during the full duration of said activity. The objective of this research is to develop a framework for optimizing the allocation of multi-skilled labor in construction projects by allowing partial allocation. The novelty of the presented methodology is that it allows for partial allocation, which further minimizes the idle times of labor when compared to a conventional multi-skilled labor allocation model. With the use of genetic algorithms, the model was applied to a case study, where the results did not only show a reduction in idle labor days, but also shifted the histogram’s end point to the left, reducing some activities’ durations and hence reducing the total project duration.

This study presents the development of a linear optimization model for scheduling repetitive construction projects with varying quantities of work at repetitive units to minimize project total cost. The model is capable of identifying the optimal order of executing repetitive units and considering multiple construction crews with different productivity rates. This model is developed in three main steps: (1) identifying input data; (2) formulating optimization model by identifying decision variables, formulating objective functions and constraints, and executing model computations; and (3) generating project schedule by quantifying work interruptions and cost; and identifying order of executing repetitive units for each crew. The model is designed to calculate project total cost based on direct and indirect costs. One example from the literature is analyzed to demonstrate the model capabilities and illustrate its use. The model results showed shorter project duration and cost compared to existing solutions in the literature. The model performance is verified by solving two demonstration examples from the literatures. The present model is expected to support project planners by enabling them to minimize project total cost.

Road projects around the world are widely affected by the cost overrun phenomenon. BIM can greatly contribute to cost overrun mitigation; however, the BIM adoption in road projects is limited compared to building projects. Besides, studies focused on analyzing the impact of BIM on mitigating cost overruns in road projects are lacking. Therefore, this study presents a review of road projects with BIM implementation, in which the BIM benefits that can contribute to the cost overrun mitigation are analyzed. The research method is composed of the following stages: (1) a systematic review is performed; (2) relevant studies are chosen based on inclusion/exclusion criteria; (3) qualitative evidence is categorized; (4) qualitative evidence is analyzed with a set of experts; and (5) contributions are summarized. A total of 21 road projects were studied, in which a set of BIM implementation benefits were identified. Thus, a professional workshop analyzed the interactions between BIM benefits and the main cost overruns causative factors. The findings indicate that the BIM implementation in road projects contributes to mitigating cost overruns factors, such as inadequate project planning, design changes, failures in design, project scope changes, late decision-making by the owner, and others. The three BIM benefits that most influence the mitigation of cost overruns factors in road projects are: (1) better coordination and collaboration among stakeholders; (2) improvements in decision-making processes; and (3) obtaining accurate, integrated, and detailed information.

The construction industry has been shifting from the stick-built methods to modularization. Despite such increased adoption, the industry has not yet been able to fully capture the benefits of modular construction methods due to various associated risks and challenges. While many research efforts have studied the various decision-making processes and operations related to modular construction projects, none is perceived to have developed a risk-based framework to assess the challenges associated with the use of modularization in construction projects. This paper fills this knowledge gap. In a previous research, the authors identified 50 factors affecting modular construction projects. Consequently, an industry survey was developed to quantify the identified risk factors in terms of their likelihood of occurrence, and relative impact. Based on 48 survey responses, this study conducted statistical testing to group the modular risks into risk tiers based on their statistical significance. Furthermore, a scoring system was developed as to allow for quantitative assessment of the challenges associated with the use of modularization. This research should help stakeholders make informed decisions about utilizing modularization in their projects by allowing a proactive management to the various associated challenges and risks.

Mass timber is considered as a sustainable alternative to traditional building materials such as concrete and steel. However, due to a lack of sufficient mass timber construction projects, the US construction practitioners are not familiar with the cost details of mass timber buildings. Cost is an important parameter of any construction project, and the availability of adequate cost information from previous projects helps to tackle cost-related challenges associated with building construction. This study presents comprehensive cost information of a tall mass timber building construction located in Canada. The methodology of the study was developed by comparing the budgeted construction cost of the building to the actual construction cost of the building. The budgeted cost was estimated before commencing the construction, and the actual construction cost was calculated after the completion of all construction activities. The outcomes of the study show that the actual construction cost of the building was 1.2% ($473,758) higher than the budgeted cost, including a schedule delay of five months due to failures of elevators and storefront trades. Metalworks, furnishing, and utility activities were found higher for actual cost compared to budgeted cost. Contrarily, the cost of engineered wood and plastic, exterior improvement, and installation of mechanical facilities were found lower in actual construction cost. The actual construction cost also incorporated change orders that cost an additional $2,155,549. The study will help US industry practitioners and owners to make decisions regarding their initial investments in a mass timber building project.

Mass timber is a promising building material that provides excellent structural rigidity, a low-environmental pollution footprint, and better use of natural resources. However, the adoption of mass timber is not free from challenges. Especially in the US construction industry, the material has not been accepted widely by practitioners due to inexperience and a lack of mass timber building construction projects. This study aims to determine the current awareness level among US construction practitioners regarding mass timber building material. It further identifies the existing challenges associated with the design, construction, and manufacturing process of this material. The results from 100 construction companies, 32 architectural firms, and 15 timber manufacturers show that US construction practitioners have a relatively low level of awareness and work experience in mass timber material. Among the respondents, 55% of building contractors and 30% of architects indicated that they have never been involved in a mass timber building construction project. Furthermore, a significant number of participants who were involved in the mass timber project stated that their experience level is between 1 and 5 years, indicating that the concept of mass timber is still in a very rudimentary level. Lack of timber manufacturing plants, inexperience, high cost of engineered wood, and code limitation are some of the perceived drawbacks identified by the participants. The outcomes of the study will be helpful to determine the actual feasibility of mass timber in the US construction industry.

Delivering value to the customer and to the internal and external stakeholders on construction projects is deemed essential for projects’ success. However, literature is limited in reference to the methods offered to track value alignment on projects. Defining and agreeing on what constitute project value early on would help in providing a clear starting point to capture value generation along project phases. Accordingly, this research advises first on the main steps to evaluate and classify project value, then proposes a design for a value dashboard (VDB) to visualize and track value on projects. The suggested dashboard acts as a visual management tool to guide project managers with the decision-making process considering the evolving understanding of value. The advocated value dashboard for the design phase is discussed and vetted with experts from the construction industry. Future research will further test the VDB on real life projects to demonstrates its applicability. This visual dashboard is a novel tool that provides tracking for value generation and delivery, and it is complementary to the existing project management dashboards that focus on schedule and cost considerations.

The weight of a slab usually accounts for the highest percentage of the total weight and consumes more time for its construction. Therefore, many attempts have been conducted to reduce the dead weight of the slabs and the construction time. Most of the studies focus on the traditional reinforced concrete slabs though there are no studies conducted on reducing self-weight of composite slabs with plastic bubbles. In this study, a developed type of one-way composite slab in which the uses of recycled plastic bubbles has been proposed in order to reduce the self-weight of the slab, CO₂ emissions, and embodied energy. Nine specimens of one-way composite bubbled slabs with the dimensions (1,850 mm × 500 mm × 110 mm) have been evaluated with different parameters including the geometry of the profile steel sheet (Trapezoidal shape, triangle shape, and T-shape). The presence or absence of plastic bubbles, and diameters of plastic balls range between 70 mm and 86 mm. The results showed that the composite bubbled slabs with diameter 70 mm give ultimate load close to composite slabs without bubbles except the composite bubbled slabs with trapezoidal shape showed a decrease in total load by 10.9%. Also, the results showed that the increased diameter of bubbles from 70 mm to 86 mm leads to an increment in the ultimate load capacity of composite slabs with trapezoidal shape and triangle shape by 10.2% and 18.1%, respectively. Whereas, the composite bubbled slab with T-shape decreased in ultimate load capacity by 6.2%. Finally, the composite bubbles slabs reduced weight by about 6.75% to 14% and decreased CO₂ emissions up to 13.8%.

Value in megaprojects is influenced by the complex morass of stakeholders involved who shaping complex networks that are central to collaborative project-based value creation. Although interest in the value creation of the megaproject is mounting, little is known about how stakeholders engage with the megaproject delivery process and realize value creation. To unveil this intricate process, this study presented a novel approach extended from stakeholder value network (SVN) to conduct both qualitative and quantitative analysis value creation among stakeholders in megaprojects. Built upon 99 value flows among 10 stakeholder types during the project construction stage, a qualitative megaproject SVN (MSVN) model was first constructed. The questionnaire survey was then used to quantify the MSVN model. Critical value cycles were identified for value creation, which provides effective suggestions for megaproject value management practices. This research contributes to understand the value creation of complex stakeholder networks that strengthen value enhancement in megaprojects.

Transportation Agencies (TAs) utilize Constructability Reviews (CR) to enhance project performance by introducing construction knowledge into the design process. The North Carolina Department of Transportation (NCDOT) conducts CR meetings for selected projects at approximately 70% of the design completion. This paper evaluates critical construction project factors that dictate the need for a CR. To determine these factors, the experience of previous CR participants, consultants, and contractors is needed. A survey was designed to identify project characteristics that indicate the need for a CR. This survey was sent to past CR participants (NCDOT employees, consultants, and contractors), and written responses were received. Meetings were held with all survey respondents to further discuss their written responses. A set of factors were identified by the respondents, which were then classified by category. These factors are reported herein and can be used to identify which construction projects could benefit from a CR, thus enabling the identification of infrastructure projects that may require greater scrutiny to receive it to ensure that they meet cost and schedule goals.

The world is experiencing an increasing quantity of plastic wastes, which is becoming a major environmental challenge. Recycled plastics have found their use in construction by replacing natural aggregates in concrete. However, they are not yet well-employed due to the lack of financial studies related to their use. This research aims to evaluate the life cycle cost implications of using recycled plastic based lightweight aggregate (PLA) for applications in concrete structures. Multiple design parameters of conventional concrete and concrete with PLA were used alternately in 24 different scenarios. Using PLA in construction reduced the costs of concrete and steel by 7.23% and 7.18%, respectively. The overall life cycle costs’ reduction reached 8.9%. It was also found that slabs with lengths spanning around four and five meters have higher cost-benefit from using PLA. This study’s findings contribute to the environmental conservation, body of knowledge, and ideological change in the construction field and material development.

State Departments of Transportation (DOTs) are currently facing a critical challenge of an increased demand for highway system construction work with limited available inspection resources of staff and funding. To address this issue, many DOTs require contractors to submit quality control (QC) plan and use contractor data for the acceptance decision. However, there is a lack of understanding how QC plans and data can be used for the agency acceptance decision, which may expose inspection processes and the highway pavement to the risk of quality shortfall. To address this gap, a comprehensive desk scan of quality assurance (QA) programs of the 50 DOTs was conducted to identify main components of QC plans. Seven case studies from Florida, Idaho, Kansas, Nevada, North Carolina, Utah, and Virginia DOTs were conducted. Content of QA documents was analyzed to identify and evaluate requirements for QC plans and how to use QC plans to administer QA specifications for highway construction projects. The result shows that requirements for QC plans development and implementation vary from one DOT to another. QC plan implementation resources such as staff, testing laboratories, and equipment must be approved by the DOT. The findings from this study also indicated that accelerated schedule of alternative contracting delivery methods could impact completion of adequate QC processes. This study may help both researchers and practitioners better understand the process of developing and overseeing an effective QC that increases the value of inspection and enhances the quality of construction projects.

Local conditions at a project construction site can affect the quality and durability of cementitious materials. Engineered Cementitious Composite (ECC) material is no exception. ECC is a low-volume fiber composition material known for self-healing, high ductility, and high-tensile strength. To quantify the ability of ECC to withstand differing site and environmental conditions, the material was exposed to varying temperatures and precipitation levels/relative humidity. Testing was also performed with materials that are local to the desert Southwest US. Preliminary results indicate that ECC performed well when subjected to a low humid, dry condition, frozen, and finally returned to a low-humid environment. This represents the type of environmental conditions this material might experience in high desert mountain areas of the Southwest US. In contrast, ECC performed poorly when exposed to dry conditions, frozen then placed in a wet and humid environment. Further study of varying construction site conditions is necessary to understand the performance of ECC in full-scale applications. The findings of this research contribute to a deeper understanding of the constructability of ECC under varying site conditions.

Buildings have a significant and continuously increasing impact on the environment because they are responsible for using a considerable number of resources and a large amount of energy besides emitting a large portion of carbon. In recent years, numerous attempts have been made to reduce these harmful impacts of construction activities to achieve sustainable development goals. Careful selection of buildings materials has been identified as the first step for designers to incorporate sustainable principles in building projects. As far as sustainability is concerned, façade plays a prominent role in high-rise buildings. Therefore, criteria for selecting sustainable facade materials of high-rise buildings should be studied in order to have sustainable buildings. In this paper, a comprehensive list of sustainability criteria for selecting façade materials is presented through extensive literature review and expert interviews; afterward, these criteria are prioritized using Fuzzy Analytical Hierarchal Process (FAHP) model using a survey. The study’s results showed that the environmental criteria are of the highest importance from the point of view of experts. Followed by the economic, technical, and social criteria group respectively. The result of this study could help decision-makers in selecting façade materials during design phase.

The frequency in occurrence of a natural disaster is increasing rapidly. The rate at which the families recover from these disruptions and return to normal day-to-day operations is very essential. The loss of housing and the need for temporary shelter is the immediate after effect of a natural disaster. The objective of this study is to analyze whether 3-Dimensional Printing (3DP) can be a viable alternative to the conventional process of providing shelters and housing to displaced people. The conventional process of disaster relief has many challenges, and 3DP technology provides an opportunity to provide immediate, cost-effective relief to people whose homes were severely damaged or destroyed by a disaster. Investing in the 3D printing of temporary shelters may not seem prudent to stakeholders at this time, but the growth of the use of 3D printing and additive manufacturing in the construction industry will play a key role in accelerating the reach of this new technology to disaster-damaged areas. The potential of 3D printing for producing and rendering temporary housing/shelter after a natural disaster is assessed in this paper from different perspectives such as cost, time, safety, environmental impact, and transportation logistics. The authors of the study performed a thorough literature review to understand the recent technological advancements related to 3D printing and then evaluated the opportunities and challenges of adopting the technology in disaster-struck regions for the purpose of shelters and housing. The findings of this study will guide decision-makers in evaluating the feasibility of applying 3D printing in disaster-struck areas.

The research aims to identify WELL concepts that support office building occupants’ health, well-being, and productivity in a developing country. Individual semi-structured interviews were conducted to collect data from occupants working in office buildings. Thematic analysis was used to analyze the collected data. In addition to the existing ten concepts in WELL Building Standards, three new WELL concepts have been identified: “Security and Safety,” “Building Services and Maintenance,” and “IT Infrastructure” are significant in supporting occupants’ wellness for office buildings in developing countries. Compared to developed countries, security and safety are highly concern due to the high-crime rates. Additionally, poor building services and maintenance and low support IT Infrastructure are lacking in sustaining the occupants’ wellness. Hence, this research revealed office building occupants’ needs in a developing country through the WELL concepts. These findings can assist government bodies of developing countries in streamlining WELL rating tools.

This paper presents a theoretical framework and examples of a practice called “mistakeproofing” or “poka yoke” (in Japanese). Mistakeproofing is a concept integral to lean production, and it is also related to Prevention through Design (PtD) and resilience engineering. Mistakeproofing means to either reduce the likelihood of a mistake occurring in an operation or product, or to alleviate the impact an occurrence may have. This is achieved by judiciously altering the operation or product design. While it may be impossible to eliminate all occurrences of mistakes, any occurrence should be prevented from turning into a defect. Examples from civil engineering design and construction are illustrated, described by the concern addressed and the countermeasure offered, and characterized according to the six principles of mistakeproofing. Although these examples may look familiar to readers, a new perspective is offered by viewing them through the lens of mistakeproofing. The contribution of this paper is presenting the concept of mistakeproofing with its theoretical framework, and illustrating the use of that framework with civil engineering examples. The goal is to increase reader awareness and inspire purposeful, systematic implementation of mistakeproofing practices as they lead to improved operation and product quality and safety performance.

This paper investigates how hospitals can achieve increased flexibility and adaptability using a modular adaptable hospital design (MAHD) methodology. MAHD can be used to improve hospital lifecycle operation, including a potential implementation strategy and feasibility analysis. Drawing from 27 semi-structured interviews and design review sessions with 19 international groups of healthcare, design, engineering, and construction experts, the paper proposes a MAHD Design Methodology framework. This includes an evaluation framework composed of five design categories and 23 sub-categories complemented with a set of design guidelines developed by the researchers. The resulting design guidelines explain the process of designing and building hospitals with new MAHD technologies. The paper concludes by identifying a future implementation pathway for MAHD with the ultimate goal of applying the new modular adaptable design and construction techniques in a real hospital project. The contribution is a methodological approach for designing and constructing hospitals that can achieve greater economic, social, and environmental benefits due to non-disruptive lifecycle adaptability.

Materials tracking by means of auto-identification technologies has been limited to the semi-automation of onsite materials management. Thus, an opportunity to leverage the use of identification and information technologies by materials vendors and suppliers, i.e., upstream materials tracking, is yet to be explored. In response, this study aims at characterizing the unexplored facet of upstream tracking by vendors and suppliers and explaining its barriers and drivers. A survey instrument was designed to determine the current state of materials tracking readiness, digitization barriers, and opportunities. In total, 50 survey responses were obtained. In addition, remarkable case studies on upstream tracking were characterized through the qualitative analysis of semi-structured interviews with a pre-qualified sample of 13 subject matter experts. Drivers, benefits, and barriers associated with upstream tracking were identified and characterized. For example, a lack of clear return on investment stands a prevailing barrier against the adoption of upstream tracking at every key stakeholder organization, i.e., owners, contractors, and vendors/suppliers. In addition, a disassociation between the fabrication or manufacturing information status of a material component and the physical tracking of such component was characterized as a fundamental difference from onsite tracking practices.

Energy and water efficiency measures can offer great reduction in utility costs of large existing buildings. However, decision-makers are often faced with a challenging task to identify the most cost effective measures within available budgets due to wide variety of energy and water efficiency measures. This paper presents the development of a new model that is capable of identifying optimum trade-offs between (1) minimizing upgrade cost, and (2) minimizing utility cost of existing buildings simultaneously while maintaining building operational performance. To this end, a multi-objective model is developed in three main steps: (1) formulation step where decision variables, objective function, and constraints are identified and formulated; (2) implementation step that performs the model computations and specifies the model input and output data; and (3) performance evaluation step where a case study is analyzed to evaluate the performance of the model. The primary contributions that this research adds to existing are (1) development of a new optimization model that can identify optimal building upgrades to strike a balance between upgrade cost and utility cost, and (2) implementing the model computations using epsilon-constraint method and binary linear programming to guarantee the optimality of the generated solution in short computational time. The case study results illustrated that the developed model was able to identify pareto-optimal solutions of the two optimization objectives and optimal set of upgrade measures for each of the pareto-optimal solutions for a study period of 20 years that reduced utility costs up to 39.5%.

Almost fifty million US citizens living in the coastal regions of Texas, Louisiana, Mississippi, Florida, Georgia, Carolinas, Puerto Rico, and Hawaii face hurricane or other storm threats quite frequently. The massive evacuation of a city or even a small town is threatened by the heavy traffic and time-limited. Realizing the problems with evacuation load, in-residential storm shelters that provide life protection are popular in no flood zones. Federal Emergency Management Agency (FEMA) has developed and published several storm shelter guidelines. This article did a detailed literature review of in-residential storm shelters in history and conducted a market survey for the current products. Several important factors have been analyzed, including capacity, weight, cost-effectiveness, installation, and risk performance. Through the reviews, the authors found that most storm shelters mainly paid attention to improving structural designs during the past ten years. Compared with other building material markets, there are fewer research updates and breakthroughs in developing new materials for shelter-related products. As a result, storm shelter products using advanced construction materials can be said rarely. This article details the possibility and necessity of upgrading product materials for the products of aboveground residential hurricane shelters.

Rapid global population growth is leading to an increase in demand for raw materials. The construction sector is responsible for a major part of global material consumption, accounting for approximately 50% of raw material extraction. Although the demand for construction materials is increasing, natural resources are becoming scarcer. One solution to mitigate the pressure on natural resources is to implement urban mining strategies. These will require demolition, disassembly, and deconstruction processes that belong to the broader construction domain as it evolves toward a circular economy in the built environment. Significant work has been conducted on measuring and managing the availability of secondary materials in material banks. However, there is a lack of understanding of the market value of these material banks and their potential for decreasing raw material consumption. In this research, a framework that can be used to estimate the reuse and recycling market value of in situ materials is developed. The results of this research aim to positively impact the transition to a more circular economy in the built environment by bringing insight into the value of materials that are currently in-use but have the potential to support future demands.

Construction waste recycling (CWR) is one of the solutions that address construction waste issues. As various strategies target the enhancement of CWR implementation, stakeholders face difficulties in selecting the right strategies. Understanding the effectiveness of the strategies can guide stakeholders in making better decisions. Therefore, this study evaluates strategies that target the enhancement of CWR implementation. To do that, CWR enhancement strategies were identified through a systematic literature review. Then, the identified strategies were inserted into a questionnaire survey and distributed to project managers. Finally, 108 responses were collected and analyzed using the factor analysis and fuzzy synthetic evaluation (FSE) techniques. The factor analysis grouped 12 out of the 13 enhancement strategies into two interrelated groups: direct and indirect enhancement strategies. However, the FSE technique indicated that the overall effectiveness of the enhancements strategies is only between neutral and high. This is due to the effectiveness of both groups is also only between neutral to high. This study contributes to the literature by evaluating existing CWR enhancement strategies. The findings allow practitioners, policymakers, and researchers to justify developing new and alternative enhancement strategies for CWR.

Subsurface investigation is one of the most critical phases of any construction project. An insufficient subsurface investigation is the primary source of change orders in 20 to 50% of infrastructure projects and may lead to costly failures due to uncertainties during the construction phase. Due to inherent limitations of conventional subsurface investigation methods, characterization of soil physical parameters using quick and non-invasive soil investigation methods (e.g., electrical resistivity) has been of interest to researchers. Developing proper relationships between electrical resistivity and geotechnical parameters helps to characterize the soil parameters at any location/depth. This paper aims to investigate the effects of water content, dry unit weight, fine fraction, and clay fraction on the electrical resistivity of sandy soils and create appropriate models for estimating them based on the electrical resistivity values. The linear regression analyses were performed using 100 observations obtained from laboratory testing (physical property and electrical resistivity tests). The results show a significant relationship between water content, dry unit weight, and clay fraction with electrical resistivity. The results also show that an increase in water content, dry unit weight, and clay fraction results in a decrease in the electrical resistivity. These findings contribute to the body of knowledge by identifying the influencing geotechnical parameters and quantifying the relationships between electrical resistivity and geotechnical parameters of sandy soils. It is expected that this research helps construction engineers and managers to have a rapid and continuous assessment of subsurface characteristics in the field to avoid costly failures.

The overexploitation of natural aggregates renders their use in concrete as unsustainable resources. Because of that, researchers have been studying the use of processed aggregates in concrete. However, little has been reported on how using such aggregates affects the building’s sustainability in terms of the LEED certification. This study aims to explore the benefits of using green processed lightweight aggregate (PLA) by determining the LEED requirement for Building Design and Construction and providing a comprehensive analysis of LEED credits based on the existing technical materials data. This study focuses more on the environmental benefits and provides analysis on how material properties affect its sustainability in a quantitative measure, which is the LEED scoring. The findings indicate that the use of PLA as a replacement for aggregate in lightweight concrete contributes significantly to 8 points out of 110 required in LEED certification. In addition, the paper provides the guidelines that enable developers and stakeholders in the construction industry to improve the financial and environmental benefits of using recycled materials for building and increase LEED scores toward sustainable development.

Project Development Process (PDP) is crucial for any state DOT to effectively meet its state transportation needs. Infrastructure projects are often complex and can pose significant challenges, including an incomplete and changing project scope, environmental and permitting requirements, and incomplete or inconsistent development processes, resulting in time and cost overruns. This paper focuses on the methodology to investigate and map an agency’s preconstruction PDP activities and development sequence to document current practices. It provides guidance to determine key PDP tasks, sub-tasks, and activity sequence for the agency’s various program/project type and funding source(s). The research team developed a five-step methodology to map PDP including a detailed review of state DOTs PDP and related literature followed by interviewing the Subject Matter Experts (SMEs) from various DOTs. Data were collected, coded, and analyzed to prepare PDP flowcharts for the agency. These flowchart tasks were then validated through a two-day focus group with a DOT leadership team. The PDP flowcharts for the case study DOT are based on program/project type, funding source, and environmental requirements.

The bountiful advantages of facility standardization have been previously identified and successfully implemented by the shipbuilding and manufacturing industries. However, capital projects have been unable to achieve desired levels of standardization or reap the subsequent benefits. Here, the researchers have made an effort to help capital projects attain greater levels of standardization by better understanding and recognizing the value of standardization. This paper encapsulates that effort by summarizing six solution pieces: (1) innovative technologies and management approaches, (2) critical success factors and enablers, (3) the work process, (4) a business case analysis model, (5) a decision-making model, and (6) standardization economics. The researchers compiled these solution pieces using a mixed-methods approach. The paper also proposes industry maximization enablers that show practitioners how to achieve stepwise improvement in the implementation of standardization. The paper will contribute significantly to a better understanding of standardization strategy, as well as add to the existing literature.

Construction projects involve multiple stakeholders that make numerous decisions during the design and construction phases. One of the crucial decisions is to select the most suitable building material for the project. Therefore, it is important to select a building material with low-environmental impacts. The decision-making process is not often managed in an organized manner. Many decisions are made without having a systemic approach to decision-making, thus generates conflict among the stakeholders. To overcome the challenges associated with decision-making, the study proposes a scientific decision-making framework called Choosing by Advantages (CBA). This process facilitates effective decision-making by aligning practical knowledge and subjective judgments. In this study, CBA is used to determine the feasibility of mass timber in the US construction industry. Concrete and steel were used as alternatives to compare with mass timber. Mass timber is a new form of building material that is still gaining momentum in the US market. Thus, it is nontrivial to evaluate the feasibility of this product. A total of 29 construction industry practitioners from different parts of the country participated in the study. The outcomes of the study provide evidence that the CBA tabular method is capable to select the best-suited building material by establishing the preference of the industry practitioners. It further stretches that although mass timber is not the most suitable alternative in terms of present situation, it has multiple positive features to be accepted as a mainstream building material in the US construction industry.

Evaluating the performance of construction projects has traditionally been undertaken by studying some classic performance metrics, such as cost and schedule, that are typically assessed separately. Even when these metrics are combined, it is done in the form of some opinion-based performance scoring that is inherently subjective. To address this issue, this paper presents the development, mathematical formulation, and validation of the Project Overall Performance (POP) model; a comprehensive data-based model that evaluates the performance of construction projects from the contractors’ perspective. The POP model uses quantitative performance metrics spanning five key performance areas, namely cost, schedule, quality, communication among project teams, and change management, to assess the performance of construction projects. The novelty of the POP is that it uses performance data from 32 construction projects to derive the weights (relative importance) of the performance metrics and areas. Further, POP was correlated to projects’ overhead and profit (O&P), such that contractors may use POP to understand why or how a project’s O&P performs as expected or not as expected. The results of the POP scoring and analysis process showed that change management has the greatest impact on the POP score at 32% followed by schedule performance at 24%.

Construction competencies are combinations of skills, knowledge, technologies, other resources, and practices of a construction organization that contribute to increased effectiveness, competitiveness, profitability, and performance. Previous studies have developed mechanisms to identify and develop construction competencies that aid in performance measurement at project and organization levels, separately. In reality, construction organizations are project-based organizations with complex interactions between competencies influencing performance at different levels. The challenges associated with multilevel construction competency measures include identifying the interrelationship between competencies at different levels and relating multilevel competencies to multilevel performance measures. To address these challenges, this paper provides a review of the literature related to multilevel construction competency frameworks and performance measurement methods. Based on an analysis of the literature, a multilevel framework is developed and presented for construction competency and performance measures. Finally, a data collection approach is provided that will assist researchers and industry practitioners in evaluating construction competencies and performance.

Energy projects are essential to serving the world’s growing energy demands. However, energy construction projects encounter controversy when the community disagrees with the development process or expected project outcomes. These conflicts can and often do lead to schedule delays and cost overruns, yet energy project developers are rarely prepared to anticipate, mitigate, and respond to arising challenges. With this work, we aim to model the economic consequences of controversy stemming from public opposition to energy construction projects. We propose a framework to determine the potential costs and schedule delays occurring during a project’s timeline. We use a hybrid system dynamics and agent-based modeling approach demonstrated by wind energy construction projects. Developers can use the proposed methodology by inputting their project characteristics to anticipate controversy and mitigate risk through public outreach. The resulting model will serve as a proof of concept that can be expanded upon in future research.

According to a report by S&P Global (2020), more than 2/3 of companies worldwide shifted to a work-from-home strategy due to the COVID-19 pandemic. The imposed remote form of communication renders knowledge sharing among colleagues challenging. Previous studies tackled the process of knowledge sharing but failed to assess the effect of a sudden emergency leading to remote work. To fill this gap, this paper proposes an integrated methodology to understand how the transition to a virtual work setting affects the knowledge sharing process and drivers. A survey targeting engineering companies is used to capture the work-from-home experience. The survey focuses on personal and external drivers affecting knowledge sharing intention, behavior, and outcomes. Personal drivers include motivation, enhanced personal relationships, and knowledge feedback. External drivers focus on the effectiveness of information and communication technology (ICT) in facilitating knowledge transfer. The results show that traditional factors such as enhanced personal relationships become less influential in remote work environments while ICT becomes more appealing. Incorporating these preliminary results with advanced statistical studies will enable companies to plan and implement strategies to foster knowledge sharing in different environments.

Risk management is a part of a comprehensive project management approach. Properly identifying, analyzing, and determining the appropriate responses to risks gives project teams a degree of control over uncertainty and leads to better outcomes. Comprehensive risk identification serves to minimize the unknowns faced on the project. While every construction project is unique, there are similarities between risk statements across projects. How effectively are these risks captured? This study examines design and construction risks on scientific research facility projects delivered by the US Department of Energy (DOE). These projects provide a fertile area of exploration because they deal with first-of-a-kind design and construction in uncertain environments. The study uses content analysis techniques to review risk registers across multiple projects. This study proposes a Risk Breakdown Structure (RBS) for these projects as the basis for comparing risks between projects and improving risk identification. The paper provides a foundation for a more complete risk identification step and describes the links between certain project characteristics and their associated risks.

Public housing (PH) provides a substantial portion of the housing in cities, and the need for it is increasing. PH significantly affects its users’ lives and society; thus, providing sustainable public housing (SPH) is critical to ensure sustainable communities and meet sustainable development goals. Many techniques have been developed to improve sustainable performance in construction projects. However, there is no integration checklist to assess the sustainability performance of SPH. Also, the techniques used to evaluate the sustainable production and performance of construction projects seem ineffective. There is a need to apply a holistic framework that engages all the stakeholders to assess the sustainability performance of SPH. Implementation methods should be developed to ensure the effective application of the framework. This study adapted the Triple Bottom Line plus one (TBL+1) as a comprehensive framework to evaluate PH. This paper aims to develop an integrated sustainability performance criteria checklist (ISPCCL) as a guide to assess sustainability performance throughout the PH lifecycle based on the TBL+1. The project ISPCCL helps understand the significant factors affecting a project’s sustainability performance and balanced project stakeholders’ needs, values, and input to achieve a satisfactory SPH project.

Complex stakeholders’ relationships and the project risks interactions exist in today’s construction projects due to the increasing project complexity, especially in mega projects. In order to identify critical risks, it is imperative to clarify the stakeholders’ relationships, the process of risk interaction, and how stakeholders’ behaviors influence the occurrence and the controlling of risks in the project. The purpose of this work is to evaluate the stakeholders’ impact on the project risk interaction by building a two-layer network model and promote the effectiveness of risk control through identifying the key network nodes. The two-layer network model includes a stakeholder relationship network (SRN) and a risk interaction network (RIN), as well as the links between the two networks. The key to building SRN-RIN two-layer network for a construction project is identifying three relationship sets, includes the stakeholders’ relationship, the risk interaction relationship, and the relationship between stakeholders and risks. An illustrative example is conducted to illustrate the analysis process. This work advances the research on risk interaction in construction projects by incorporating human behavior. The findings will provide support for project managers to improve their risk response decision, which will contribute to the construction project success.

This review examines how human values have been conceptualized in sustainable construction and the primary considerations for the study, management, and application of values to the built environment’s transition to sustainability. “Values” are understood as “assets” and/or “goals” in the sustainable construction literature. These meanings are mediated by the triple bottom line model of economic, environmental, and social sustainability and by the ideals of benefit maximization, balance, and optimization. The review also revealed that sustainability has been defined as an adaptive system of values, and three approaches to it were synthesized: essentialism (alignment of stakeholders’ values with an absolute ethical framework), pragmatism (negotiation and use of already held values), and critical approach (empowerment of non-governing stakeholders). Findings support the idea that human values and the built environment are subject to an ongoing mutual reconfiguration. Findings also suggest how already-held values can be used to foster sustainability initiatives in the construction industry.

With an increasing demand for passenger and freight travel, airport construction program spending has heightened throughout the years. This poses a greater threat for sponsors to withstand the required financial capacities especially within the current economic conditions. None of the previous research attempted to provide a comprehensive overview on Airport Improvement Program (AIP) project expenditures. This paper fills this knowledge gaps using a graph theory approach. To this end, the authors used an interdependent research methodology that comprised of: (1) identifying AIP project keywords to represent the work actions performed; (2) vectorizing the data into a reference matrix that is split based on fund level; (3) developing the adjacency matrices from each reference matrix; (4) constructing the graph networks; and (5) visualizing the full data as well as interpreting for similarities and differences between the different fund levels. There were sustained similarities as well as differences between the fund levels. Results also indicated that the rehabilitation in the main aircraft entry, exit, maneuvering, and stationing areas are the main focus and center of all fund levels. Ultimately, this research synthesizes a foundation for comprehensive understanding of AIP projects by studying the interconnections as well as work actions performed in each fund level. This shall aid stakeholders in improved fund allocation decision-making procedures and management of human resources.

Adversarial relationships between different construction stakeholders have been one of the main causes of poor performance in projects. As such, collaborative planning has been presented which promotes active involvement and integration between the various parties to a project. Previous research concentrated on specific facets of collaborative planning and lacked offering a broad study of the underlying factors affecting construction collaborative planning. This paper tackles this point. As such, the authors distributed a survey to industry experts for evaluating a holistic list of 50 collaborative planning metrics. Utilizing factor analysis on the retrieved survey data of 53 responses, the authors concluded that 35 metrics can be extracted and grouped into five categories, namely, behaviors, planning, procurement, technology, and lean construction. The research findings highlight the intercorrelation of the studied metrics and thus offer practitioners with enhanced understanding of the factors influencing construction collaborative planning. Ultimately, successfully employing collaborative planning in construction projects will enhance the performance in terms of enhancing productivity, reducing cost and schedule overruns, and fostering a more collaborative work environment.

The global and national spending on public infrastructure projects continues to increase tremendously. Accordingly, contractors need to adopt efficient bidding strategies to cope with the legal requirement of competitive bidding within the public infrastructure projects. Moreover, contractors usually acquire the services of subcontractors to handle large projects. However, despite the previous research efforts on developing bidding models, there is still a lack of research that tackles the multi-stage construction bidding, where subcontractors submit quotations/bids first and general contractors bid second for the whole project. As such, this paper aims to develop a game-theoretic bidding model for multi-stage construction bidding. To this end, the authors utilized an interrelated methodology comprised of: (1) investigating existing bidding models that are based on a game theory approach; (2) deriving of a bid function for the multi-stage construction bidding following the low bid method where the lowest bidder is the winner; (3) simulating multi-stage bidding environment; and (4) validating the derived bid function and simulation model utilizing data of 808 US public infrastructure projects. Results indicate that the derived bid function gives general contractors a competitive advantage by reducing the occurrence and magnitude of earning negative profits (known as the winner’s curse) while winning a reasonable number of projects; and hence, resulting in a higher expected profit. Ultimately, this study adds to the body of knowledge by providing a bidding model for the multi-stage construction bidding that shall aid contractors in dealing with the uncertainties within the associated decision-making process.

Although stakeholder involvement (SI) can improve project performance, integrating these stakeholders into the project development process can be complex. Furthermore, there is limited guidance on when and how to involve these stakeholders. Recognizing this challenge, the goals of this paper are to identify how early SI can impact the directionality of a project and to understand what factors strengthened SI efforts. In this research, a series of exploratory case studies were performed across a number of key stakeholders on a single inland waterway project. These case studies focused on gaining insight into what impacts various engagement efforts had on the project, including how the actual timing of engagement may have changed the project’s trajectory and how stakeholders perceived the project’s outcomes. The case studies showed that SI is best obtained during the feasibility phase and could alter the project’s trajectory. In addition, solid relationships, open communication, and trust were determined to be major factors contributing to the success of the first phase of the project. This paper concludes by proposing future research that would improve subsequent phases and maintenance processes on inland waterways.

Organizational improvisation (OI) is a spontaneous process deviated from plans and routine, which is carried out by actors to deal with emerging issues or unexpected events. Megaprojects are confronted with complex and dynamic uncertain circumstances, leading to the requirement of distinct OI conducted by multi-level stakeholders. However, current research primarily focused on OI at the individual level in ordinary projects neglecting the multi-level and degree characteristics of OI in megaprojects. To systematically understand OI in megaprojects (MOI), this study introduced classic OI theory to megaprojects by constructing a three-dimension MOI framework, involving improvisation degree, organizational level, and project stage. An inductive approach was utilized via expert interviews and 424-event collection to consolidate the framework. The result demonstrated that MOI most frequently occurs at the construction stage, during which minor improvisation at the team level is the most common, followed by minor improvisation at the individual level, and bounded improvisation at the team level. This research extended OI theory into the construction megaprojects field by providing stakeholders with a further understanding of MOI to deal with various uncertainties.

The scope of this research is to investigate project competencies as leading indicators of project performance using two robust data-driven weighting mathematical frameworks. The objectives of this research are to (1) create a comprehensive performance score named the Project Performance Assessment (PPA) score by combining eight performance metrics spanning over six performance areas: cost, schedule, productivity, safety, quality, and communication; (2) benchmark successful projects from less-than-successful projects by performing K-means clustering; (3) compute a customized competency category-specific score from various competency factors spanning nine categories: alignment and team integration, planning and design, procurement management, contract and risk management, health, safety and environmental, quality management, labor productivity, execution and commissioning, and change management and project control; (4) quantitatively assess the relative contribution of the different competency categories to the PPA score and provide one unique comprehensive score named the Competency Category Assessment (CCA) Score; and (5) utilize a developed regression model to examine the degree of association between the CCA score and the PPA score. The results of this study can assist the construction industry in accurately identifying areas that require improvements based on an individualized assessment of different competency factors, ultimately ensuring more effective and efficient execution of projects.

Decision-making in the construction industry is extremely important as it impacts the safety, quality, time, and cost of the ever-increasing project complexity. Understanding an individual’s decision-making offers an opportunity to improve both learning and performance in the construction industry. To this effect, a psychometric instrument was developed named “Construction Decision Making Inventory (CDMI).” The CDMI characterizes an individual’s decision-making through a set of questions. This research study focuses on developing a framework to collect, analyze, and benchmark brain activities during the validation of the CDMI on measuring “How” individuals make decisions. The proposed framework is grounded on a mix-designed research methodology following the Human Subject Research protocol of two Texas universities. The framework includes measuring the brain activity of a group of participants while completing a face value validation of the CDMI. The framework includes: (1) Consent information, (2) Set-up of the functional near-infrared spectroscopy (fNIRS) to measure brain activity, (3) Stroop Test (Congruent and Incongruent), (4) Relaxation period, and (5) CDMI face value validation. The data collected from both the fNIRS and the CDMI face value validation instrument are then statistically correlated using a timestamp. Additionally, Rational-Experiential Inventory (REI) fNIRS data are included in the statistical correlation to validate the results. The results of this study are innovative and important as it provides the first insight into using fNIRS brain activity measurements to validate a psychometric instrument (such as the CDMI) to provide some light into “How” future construction professionals make decisions.

The Project Definition Rating Index (PDRI) Maturity and Accuracy Total Rating System (MATRS) tool was developed to guide front end planning (FEP) implementation, help project teams identify gaps in their project planning, and ultimately improve project outcomes. This said, not all elements of FEP are created equal. The purpose of this paper is to share specific factors found to impact cost increases and schedule delays. The results are based on a statistical analysis of project data collected from 32 industry experts representing owners and contractors. FEP factors found to impact costs growth included lack of definition of market and project strategy, equipment, procurement, commissioning, labor costs, as well as scope creep. Factors affecting schedule change included lack of definition of procurement, detailed design/engineering, risk management, permitting, commissioning, surveying, and again scope creep. Guidance to utilize PDRI MATRS will be given to practitioners concerning consistent and reliable decision-making early in the project lifecycle.

Pre-demolition planning involves a tradeoff between recyclability and productivity due to the factors affecting different stakeholders. Demolition contractors hope to maximize their profits by selling salvaged materials to recycling markets and avoid the disposal fees by diverting the waste from landfill sites. This may result in an extended duration of the demolition phase of the project due to the use of labor-intensive demolition methods that yield more salvageable materials. Owners and general contractors, on the other hand, try to avoid such extensions in their demolition plans as they can result in project delays and additional costs to the construction project that follows the demolition phase. Although these two opposing ideas (i.e., recyclability and productivity) serve as driving factors for pre-demolition planning, researchers and practitioners have rarely considered the tradeoffs in pre-demolition planning. This paper develops a framework to find the optimal balance between recyclability and productivity in pre-demolition planning, which determines combinations of different demolition methods (i.e., saw cutting and standard, mini, and high-reach excavators) to demolish different sections (or buildings) in a project. Linear programming is employed to find the demolition plan that maximizes the profit from salvaged materials within time constraints. Specifically, the developed model considers different costs associated with the demolition project, including clean fill disposal, landfilling, and direct demolition costs, along with the project’s scheduled time constraints. For demonstration, we implement the proposed framework to guide the development of a demolition plan for a project in the Midwestern United States.

Knowledge sharing behavior is considered to accelerate information exchange among stakeholders and promote project performance in megaprojects. The motivations of megaproject stakeholders to implement knowledge sharing behavior could be divided into two classifications: economic motivation and social motivation. Environmental dynamism is often considered as unpredictable events in the environment, such as significant technological changes, boycotts by environmentalists, and so on, which could influence the effect of economic motivation and social motivation on knowledge sharing behavior in the megaproject. This paper investigates whether environmental dynamism mediates the effects and to what extent this relationship is strengthened or weakened. A total of 188 professionals participating in various megaprojects were interviewed through the questionnaire survey, and the Smart Partial least squares regression (PLS) technique was employed to analyze the data. Hierarchical regression results suggest that environmental dynamism has a strong moderating effect on the relationship between social motivation and knowledge sharing behavior. The findings contribute to understanding the effect of environmental dynamism on knowledge sharing behavior.

A myriad of problems has characterized social infrastructure issues in developing countries, highway pavement failure is identified as one with recurring problem hampering connection and trading relationships among neighboring countries. Although, such issues are gradually been annihilated in developed countries with evidence of some pavement construction reaching life cycle expectance before surface or structural failures. Developing countries continue to struggle with such myriad of problems with new construction posited to experiencing same. Therefore, this present study foreshadows existing research and results from developed countries, to investigate automation assessment readiness (AAR) for highway construction processes in developing countries. A quantitative method using questionnaire was utilized to achieve this objective. Based on the identified indicators of automation readiness, a survey of construction practitioners (in Nigeria and Jordan) was conducted to appraise the current situation and confirm readiness level that will spur automation adoption for developing countries. The study result ranks economic benefits as the most critical indicator and a readiness score of 80.9% to AAR that will help curb recurring pavement issues in developing countries. Finally, the study proposes a path for developing countries highlighting a fundamentally AAR adoption process for highway construction.

The architecture, engineering, and construction (AEC) industry is introduced to a lot of innovations and changes in various types such as technology (software and hardware informational systems), management process (alternative project delivery, alternative procurement methods, and process improvements), and business structure (mergers, acquisitions, reorganizations, prefabrication, etc.). The industry is rapidly adopting different types of changes. The objective of the study was to determine if certain types of change are harder than others to successfully adopt and implement. An industry-wide approach was taken using an online survey methodology to collect more than 500 cases of organization-wide changes from AEC firms across the United States and Canada. The method of analysis includes reliability testing, principal component analysis, and group differences. The results showed that successful adoption rates of different types of change were not significantly different for certain change types than the others. Further analysis was performed to determine if different demographical considerations of adopting organizations (type and size) had different rates of successful adoption of change. The overall successful adoption rates were generally consistent between different demographical considerations of adopting organizations, but there were minor differences. The discussion addresses those minor differences and provides possible explanations. For example, higher rates of successful adoption were found in specialized firms (roofing contractors, plumbing contractors, etc.) when compared to wide-focused firms (general contractors, EPC firms, etc.). This study contributes an industry-wide view of successful change adoption rates between different types of changes and different demographical considerations of adopting organizations in the AEC industry.

The adoption of Lean Construction (LC) methods at the trade level is lagging, despite the considerable benefits of implementing these methods for contractors. This suggests the need for better approaches and practical strategies to support lean adoption by trade contractors. This paper seeks to understand the current status and trends of lean in the trade contracting environment by interviewing lean experts. In total, 27 semi-structured interviews were conducted with construction practitioners to explore the benefits, current methods, and factors required to successfully implement lean methods by trade contractors. Subsequently, content analysis was performed to identify indicators that assist in implementing lean approaches, as well as barriers hindering trade adoption of lean. A total of 35 unique benefits were categorized into five main groups: Business Case, Outcomes, Process, Team, and Workforce. Additionally, the study found that 12 different barriers in five categories, including cultural and human attitudinal, management commitment and support, skill and knowledge, fragmentation and procurement, and resources, were linked to the successful implementation of lean practices. Ultimately, practical recommendations to guide trade contractor adoption of lean are presented. This study finds that despite the potential benefits of achieving a higher level of productivity and profit through implementing lean practices, there is still a shortage in uptake and consistency of implementation required to attain these benefits.

The discrete time-cost trade-off (DTCT) problem has been solved or nearly solved using exact, heuristic, or meta-heuristic methods. However, the existing current methods have a certain level of limitations in optimizing the DTCT problem in terms of either computational time or accuracy as efficiently as is beneficial for large project networks in the construction industry. For this reason, further research into new methods for solving DTCT problems and improvements to the methods in practical use is required. The study in this paper presents a new heuristic method of sectional network compression to solve DTCT problems in practical computational time while promising the accuracy of time-cost trade-off results. The case study demonstrated that the section network compression process controlled the time-cost trade-off operation at the acceptable level of cost and duration accuracy. The section network compression process is the novelty introduced to the heuristic method to advance its time-cost trade-off operation performance.

Construction is a complex trade requiring effective resource management and coordination for successful project delivery. Uncertainty coupled with tedious coordination can be mitigated through an efficient project manager (PM). Even though a plethora of research has been conducted to assess the performance of general contractor’s (GC’s) PMs, there remains an opportunity to create a standardized tool to assess the owners’ PMs (O-PMs) performance, which this research addresses. As part of an ongoing research study, this paper explores the responsibilities of educational organization O-PMs through a detailed literature review, followed by interviews of higher-level managers to further elaborate on their responsibilities and finalizing a list of key performance indicators (KPIs). This research is intended to facilitate the feedback process for the O-PMs and encourage the use of this assessment for positive reinforcement by identifying areas of strength and weaknesses in PMs’ performance to provide a constructive feedback process and encourage a healthy learning environment.

The objective of this research is to identify the practices and strategies state DOTs employ to cope with the challenges they faced during Right of Way (ROW) acquisition process and to discuss how those practices could be effectively organized to expedite acquisition processes. Transportation agencies often face delays in ROW acquisition because of the unexpected challenges during the acquisition process. Therefore, it is essential to develop methods to overcome the anticipating issues so that the delays can be prevented beforehand. Interviews with the ROW office representatives in nine state Departments of Transportation are performed to determine the existing challenges and identify the best practices to mitigate the challenges and expedite the ROW acquisition process. The identified practices are categorized along the following five areas of ROW acquisition process: (1) practices in the early stage of project, (2) develop ROW plans, (3) prepare and review appraisals, (4) property acquisition and relocation assistance, and (5) training and peer reviews. This research contributes to the body of knowledge in identifying challenges associated with ROW acquisition process for transportation projects and determining practices to mitigate the issues and to expedite the acquisition process. It is anticipated that the findings of this research will provide insights to ROW practitioners on what can be done better to improve and expedite their acquisition process.

People’s resistance to change has been identified as one of the critical challenges that inhibit the promotion and implementation of new technologies in the construction industry. Researchers have propounded various strategies to mitigate people’s resistance to technological change. However, most of these assertions are based on the researchers’ opinions which should be supported by qualitative evidence. Accordingly, this research adopted a Delphi Survey to solicit the views of a purposively sampled 7 member expert panel assessing the effectiveness of resistance-mitigating strategies. The results highlighted “Training” as the most effective mitigating strategy, followed consecutively by “organisational support,” “enhancing system design,” “education,” “communication,” “participation,” “peer support,” and “incentivization.” Notwithstanding this ranking, the research identified that no single mitigating strategy is optimal for completely curbing resistance. Therefore, this research also provided a framework for selecting the appropriate mitigating strategies according to different resistance scenarios. The findings of this study can better equip stakeholders of the construction industry in handling resistance to technological change.

Construction 4.0 represents the exploration of new technologies in the architecture, engineering, and construction (AEC) industry. The development of digital technologies is rapid and the adoption of them significantly impacts construction projects, for example, leading to a reduction in change orders, better decision making, and improvements in quality of work. However, stakeholders may find it challenging to determine the promising technologies within the context of the AEC industry. This paper presents an exploratory study to investigate the potentially applicable technologies and their research and practice trends in the AEC industry. A scoping review was the method utilized to perform a quantitative analysis of over five thousand journals papers published from 2010 onward based on two academic databases (Scopus and CNKI). The results present the top 10 construction 4.0 technologies, including building information modelling (BIM), artificial intelligence (AI), 3D printing, machine learning, internet of things (IoT), geographic information systems (GIS), virtual reality (VR), big data, robotics, and augmented reality (AR). Subsequently, 145 industry professionals were invited to select the most used construction 4.0 technologies in their projects via a questionnaire survey. Mobile devices, BIM, and Digital signature are mostly adopted on-site. The findings of this study can enhance the awareness of stakeholders toward construction 4.0 technologies and may help them make better decisions in selecting and implementing the promising technologies.

Organizational change is an initiative to transition from current practices to a desired future state. To ensure the achievement of expected outcome and successful change implementation, organizations often utilize different strategies and resources for similar change efforts. This study evaluates the impact of key organizational change management (OCM) practices adopted by facility management (FM) professionals. Some of the common change efforts investigated through this study include the adoption of a new technology, workspace modifications, building moves, among others. The research team adopted a survey-based methodology to collect a data sample from FM professionals. The survey aimed to capture details on organizational characteristics, change description, OCM practices implemented by FM professionals to deliver the identified change, and feedback on change adoption success. The OCM practices were derived through an extensive review of existing academic literature on organizational behavior and Architecture, Engineering, and Construction (AEC) fields. The final survey included 28 questions and was distributed to approximately 800 attendees (mostly FM professionals) at a facility conference. A total of 292 completed surveys were collected. The final dataset included details on 184 facility change initiatives with information on 13 OCM parameters, where FM professionals were either solely or partially responsible to implement the change. Through machine learning algorithm, the research team was able to identify some of the key organizational characteristics and change management strategies responsible for the successful workplace change adoption. The developed models recorded accuracies of 82% and 75% based on the validation of 30% of the randomly sampled data.

With the advancement of technology, many industries are improving their workflows and productivity by embracing digital transformation, and specifically data-centric approaches. Construction, however, is often slower to adopt new technology and innovations. A data-centric approach can enrich project delivery value and streamline processes through applications of data science and emerging innovations, such as artificial intelligence. This study aims to identify the barriers to adopting data-centric approaches in the construction industry. Initially, a literature review on Building Information Modeling (BIM) implementation identified 35 barriers. Next, the barriers were organized and refined to be more specific to a data-centric approach with the help of expert focus group meetings. Finally, the barriers were categorized based on experts’ opinions into five categories: Process, Information, Financial, People, and Risk. The result of this study can help the construction industry move toward a more data-centric approach through the evaluation of approaches to overcome the identified barriers.

The project-based nature of the construction industry requires organizations to establish temporary alliances to complete projects within required limitations (e.g., time, cost, and quality). While projects endure many risks during delivery, times of disruption (ToD), such as economic crises, extreme weather, and epidemics can create greater strains on teams, specifically hindering integration elements critical to success of project teams. Motivated by the need to improve team integration in inter-organizational and interdisciplinary construction project teams, in the light of social network theory, this study poses to identify problems regarding transaction costs (TC) and knowledge transfers (KT) during ToD: breakout of the COVID-19 pandemic. To achieve this aim, the researchers conducted and analyzed interviews with core members of a complex project team involving over 85 organizations. The results provide insights to common communication problems in complex project teams leading to suboptimal team and project performance outcomes and potential remedies to reduce TC during ToD. The insights include unique contributions to team functioning under abrupt changes to meeting modality (i.e., from in-person to virtual and then to hybrid modality during the pandemic).

Production metrics (PM) are important yardsticks for AEC (Architecture, Engineering, and Construction) teams to determine whether their projects are on track. This paper reports on research that looked for empirical evidence of the use of PM from the literature (PML) in practice. Specifically, this research classified PM reported by 279 VDC (Virtual Design and Construction) professionals in Scandinavia and Latin America into 17 PML. About 3 PML (detailed schedule conformance, stakeholder involvement, and meeting efficiency) were found to be reported by more than 40% of the professionals while 7 PML (decision latency, detailed cost conformance, field material delivery, meeting agenda appropriateness, client satisfaction, safety, and visualization) were reported by fewer than 10% of the professionals. The biggest differences in the use of PML were in measuring meeting efficiency (Scandinavia: 44%, and Latin America: 78%), meeting effectiveness (Scandinavia: 40%, and Latin America: 3%), and BIM coordination consistency (Scandinavia: 23%, and Latin America: 40%). This study not only finds empirical evidence for commonly used PML, but also finds PML that seem theoretically useful are not used in practice, suggesting further research in metrics-driven management of AEC projects.

For companies trying to evaluate their capital deployment and return on investment, an important performance metric is capital efficiency. When defining and assessing capital efficiency, though, researchers face a common challenge—how to do so in a consistent way. Capital projects in the downstream and chemicals sector require significant capital expenditures to develop. The revenue generated from these projects is directly impacted by how they operate. In other words, the development of a project and its subsequent operation have an impact on the efficiency of the capital invested. Thus, it is important to address and improve capital efficiency at the project level. Researchers in this study reviewed the literature and interviewed industry professionals to explore how their organizations define and assess capital efficiency. The research team used a focus group to develop a pathway to identify management processes that, if implemented effectively, lead to efficient capital deployment in downstream and chemical projects.

Construction teams are dynamic in nature and seldom are repeated on different projects. During a project’s lifecycle, a substantial amount of knowledge is gained by project personnel. Such valuable knowledge can be lost if it is not documented properly or if team turnover occurs. Lessons learned databases are effective knowledge repositories that can disseminate relevant project information to end-users. This article presents a review of literature that focuses on knowledge documentation and the current tools that are utilized in institutionalizing knowledge related to construction projects. The research methodology entails summarizing (1) organizational challenges that warrant the use of knowledge repositories, (2) opportunities for implementing organization-wide knowledge repositories, and (3) the latest cutting-edge technological tools and methods that are employed to utilize the knowledge stored. This comprehensive review of the existing body of work will serve as a resource for both organizations and the research community to develop robust knowledge repositories.

Characterized by fragmented nature and different conditions for every project, the Architecture, Engineering, and Construction (AEC) industry seeks remedies to achieve enhanced project outcomes including sustainability. Highly integrated project teams can overcome the obstacles by exchanging knowledge, goal alignment, and risk and reward sharing. Understanding the interaction patterns in project team networks can help implement desired practices to optimize sustainability outcomes. According to the influence theory, individuals are influenced by their interactions. They change their beliefs and practices in response to the people they connect with. Basic influence processes yield complex interactions and behavior patterns, and insights to these processes can help understand knowledge transfers in project team networks. The goal of this study is to examine the formation of knowledge transfer networks for sustainability in project teams via the lens of influence model and provide insights to complex organizational behaviors. To achieve study goal, we longitudinally collected e-mail exchange and archival data from an AEC project and performed Social Network Analysis (SNA) focusing on Energy and Atmosphere related interactions. The results showed that the exposure to the nodes with high-engagement levels and diverse expertise areas in a network positively impacts the sustainability knowledge transfers and engagement level of the individuals.

The inherent characteristics of the Architectural, Engineering, and Construction (AEC) industry require experts from various organizations and disciplines to collaborate toward shared project goals. However, it is significantly difficult to connect individuals from diverse disciplines, backgrounds, organizations, and effectively integrate their knowledge. Social network approach addresses integration and know-how flow dynamics. However, there is a knowledge gap regarding how network topologies (i.e., parameters relating to nodes, ties, boundaries, and their placements across network) help improve knowledge transfers and optimize project outcomes. Thus, this study compares the network topologies of two similar AEC project teams. We collected e-mail exchange, observational, and archival data and conducted surveys from two projects during the construction phase and performed mixed methods of analysis. The study findings showed different network topologies between the two projects and one of the project networks evolved based on the project needs which yielded better performance. The results suggest that network parameters are not meaningful alone and engagement and network topology matter in measuring AEC project team performance. The results shed light on dynamic and multidimensional characteristics of knowledge transfer networks in complex project teams and contribute to the body of knowledge by providing a methodology in studying project teams in this light.

Utility relocations are frequently cited as one of the main causes of highway construction delays. A primary goal of State Transportation Agencies (STAs) is to deliver highway projects with the least disruption possible to the public. Research and practice suggest that effective utility coordination can improve the delivery of transportation projects. Thus, the Kentucky Transportation Cabinet (KYTC) funded a project to investigate how to improve the integration between utility coordination and highway design by developing a new approach that will enhance the alignment between both processes. A fundamental step to develop this study was to achieve a comprehensive understanding of the written procedures of both processes and to analyze their current alignment. Understanding this alignment allowed the research team to develop a new approach for KYTC highway projects that involves the incorporation of effective utility coordination practices and strategies. This paper summarizes the process followed by the research team to interpret and analyze the highway design and utility coordination written procedures by developing visual representations of the information provided by KYTC guidance manuals.

Conventional hiring wisdom in the construction industry relies heavily on worker productivity for eventual promotion into supervisory positions. However, the skills required to be a successful non-supervisor and a successful supervisor vary greatly. This research identifies the differences in the necessary skill sets between male and female workers who are non-supervisors and supervisors. In this research, 65 survey participants were asked to rate their strengths as a non-supervisor for 36 skills in the categories of interpersonal skills, business/financial skills, and operational skills. If the employee was later promoted to a supervisory position, they were then asked to rate the importance of these same 36 factors while working as a supervisor. The responses were then analyzed to determine skills gaps based on gender between non-supervisors and supervisors as well as strengths in the field which could be used for the selection of candidates for promotion. The results show six shared skills gaps identified by both male and female respondents. However, male respondents identified one additional skills gap not common with women and female respondents identified five additional skills gaps not common with men. Additionally, the results found 20 shared key strengths that can be used as selection criteria for promotable employees. This research finds that a gender confidence gap exists where equally skilled females rate their weaknesses higher than their male counterparts.

The construction industry is highly competitive and actively involved in multiple economic and societal areas. Construction companies need to establish superiority over others to stay progressive in such a climate, and the characteristics of marketing and industry have prominence in company growth. This research addresses various company expansion strategies used for industry diversification by general contractors. The paper first establishes a context for the strategy comparison through the literature review of marketing hypotheses. Then, it analyzes the differentiation between four frequently used market-growth methods with the purpose to initiate a revised competition strategy for small- and medium-sized enterprises in the construction industry in the data-centric era. The data are gathered using Web Crawler technology and from questionnaire surveys and utilize principal factor analysis based on the minimum covariance determinants to compare the data obtained from the construction industry. The results describe how construction companies fulfill the previously established characteristics for their growth.

An Earned Value Management System (EVMS) is an organization’s system for project/program management that integrates a defined set of associated work scopes, schedules, and budgets, allowing for effective planning, performance, and management control. Using a mature EVMS is critical to overall project success of large complex government projects. The government customer’s expectation of a mature EVMS is to provide current and accurate data to control the project and support informed decision-making, including cost and schedule predictions. However, a comprehensive literature review reveals that there is a need for a consistent rating to measure the maturity of an EVMS. The primary objective of this paper is developing a new EVMS maturity rating, validating it on real projects, and testing its impact on project performance in terms of cost and schedule growth. The authors developed the rating based on a literature review of more than 300 references, a survey of more than 250 respondents, and workshops with more than 50 expert practitioners. The paper identifies 56 maturity attributes that are responsible for EVMS maturity and tests the new maturity framework on real projects to measure its impact on project performance. The preliminary results of data collected from 11 real-life projects, totaling $12.5 billion in project costs, show significant differences in cost and schedule performance between the projects with lower EVMS maturity scores and those with higher maturity scores.

Public–private partnerships (PPPs) have become an important financing method for urban rail transit projects (URTPs). Due to the long operation period and specialized operation tasks, competent operators are essential for the success of URT PPP projects (URTPPs). However, the government confronts a dilemma in choosing either local URT groups or non-local operators to keep both effectiveness and efficiency. In this research, we examined the key factors that determine the government’s choice of operators in URTPPs. Based on a systematic literature review, an evaluation system comprising 20 factors was first developed. Data from 73 URTPPs in China were then examined using factor analysis and logistic regression analysis. The results indicated six factors mainly relating to project features, private sectors’ attributions determining the operator selection. This study contributes to the body of knowledge on URTPPs’ operation management by providing guidance for the local government to select suitable operators and for the private sector to choose appropriate partners.

Errata identify incorrectly published items and technical errors. This file contains errata that shall be used from their effective dates; the most recent effective date is 05 26 2022.