Journal of Construction Engineering and Management

Although building information modeling (BIM) is experiencing substantial development and use in the construction industry, its legal and business structures lag far behind. While many research efforts have been devoted to promoting the implementation of BIM in the construction industry, very few of them have actually attempted to explore its contractual environment. In fact, previous studies reported the lack of, and need for, a holistic understanding of BIM contractual requirements. To fill this gap, this paper assists the various project parties in better understanding BIM contractual integration and utilization in the construction industry at different key levels: contractual incorporation of BIM addenda/exhibits, priority of contract documents, roles and obligations, copyright ownership and intellectual property rights, standard of care, discrepancies in or between models, compensation and information establishment, BIM management and collaboration, use of models, and indemnity. Within the context of documents developed by the American Institute of Architects and ConsensusDocs, the authors used a three-step research methodology that comprised (1) reviewing and analyzing essential clauses, protocols, and addenda; (2) highlighting law cases that are related to BIM contracting in practice; and (3) comparing the different BIM implementation procedures and processes. To this end, the paper presents 20 contractual recommendations to help project parties better manage BIM-based projects from a contractual point of view. To verify/validate the usefulness and benefits of the performed analysis, the authors shared the paper’s recommendations with leading experts in BIM and digital/virtual construction. Some of the specific research recommendations offered in this paper include: preparation of a list of project participants that have incorporated BIM contractual documents into their agreements to provide reasonable evidence that BIM provisions have been included in the main and affiliated contracts, and the prohibition on disclosing any received BIM data to any third party except as required by law or a court order to avoid issues with copyright ownership and intellectual property rights. This research adds to the body of knowledge by providing an enhanced understanding of various BIM contractual aspects on construction projects.

Poor safety is a perennial problem for the construction industry worldwide. The concept of safety climate has been strongly linked to safety performance, yet inconsistent methodologies make international comparisons problematic. In addressing this gap in research, a comparative safety climate survey of 515 construction operatives and managers in Australia, Indonesia, and China is presented using a standardized tool. The results highlight interesting similarities and differences between safety climate in each country and question taken–for–granted assumptions that safety climate in countries with relatively mature regulatory structures like Australia are more positive than in less developed countries like Indonesia and China. Results also highlight the intermediating effects of factors such as management commitment and cultural differences in shaping safety climate. Highlighting the potential value of theories of new institutionalism and cultural and ethical relativism, the paper concludes by raising important new practical, theoretical, and methodological questions about the merits and challenges of making international comparisons of safety climate, even when using standardized measurement tools.

A cement emulsified asphalt mortar (CEAM) made with calcium sulfoaluminate cement was prepared and tested indoors, which has a relatively slight environmental impact compared with ordinary Portland cement. The effects of asphalt to cement ($\mathrm{A}/\mathrm{C}$) and water to cement ($\mathrm{W}/\mathrm{C}$) ratios on the performance of CEAM were systematically evaluated, such as the fresh properties, mechanical properties, volume deformation, temperature dependence, permeability, and frost resistance. Meanwhile, the microstructure images and void characteristics were also obtained by computed tomography technology and scanning electron microscope. The results indicated that with the increase of $\mathrm{A}/\mathrm{C}$ ratio, the flow time, setting time, and air content of fresh CEAM increase, while the slump flow decreases. The fast-hardening CEAM has higher early strength, which decreases with the $\mathrm{W}/\mathrm{C}$ and $\mathrm{A}/\mathrm{C}$ ratios increasing. In addition, the $\mathrm{A}/\mathrm{C}$ ratio also has a significant influence on the long-term performance of CEAM. High emulsified asphalt content will reduce shrinkage, temperature dependence, and impermeability, but will increase its frost resistance. In addition, different emulsified asphalt and cement contents have a significant effect on microstructure and pore characteristics of CEAM, which directly determines its macroscopic properties. This paper contributes to the structure formation of calcium sulfoaluminate CEAM and provides a reference scheme for the construction of track slabs in winter.

Sewer pipe systems are of great importance to modern cities in various ways, making preventive maintenance a necessary activity to ensure an acceptable level of service at all times. In this respect, closed-circuit television (CCTV) inspection data for sewer pipe systems serve as the basis for preventive maintenance in the context of sewer pipe condition ratings, maintenance schedule planning, and other similar ideas. Defects (i.e., those classified as either cracks, fractures, roots, deposits, broken, or holes) and construction features (i.e., taps) are the targets of the CCTV inspection process, which is used to mark and record the defects and features in the inspection database for the purpose of developing maintenance strategies. In considering sewer pipe maintenance operations in practical terms, the following CCTV inspection data for sewer pipes are of particular interest to this research: length of the pipes, defect interval, and defect sequence for different types of defects (and taps). However, the data collection process using CCTV inspections is typically expensive and time-consuming from the perspective of the municipal department. In this context, an input modeling technique that aims to exploit the potential value of historical data is proposed by combining the Markov chain model with distribution fitting techniques and other relevant methods. The generated dataset goes through a rigorous validation process that includes statistical analysis and comparison, cluster analysis and comparison, and distance-based similarity comparison. The whole process proves that the randomly generated dataset is reasonable since it expresses similar characteristics to the original dataset in many aspects. Overall, the research proposes an input modeling process that could generate human-made sewer pipe inspection data that inherent the major characteristic of the real-life data. The generated data could benefit the real-life practice in various ways, especially in the context of data deficiency.

Throughout the design of a new building it is essential that all involved stakeholders understand, communicate, participate, and collaborate with each other to acquire a high-quality result. To support these shared and creative design processes, this paper presents the design and evaluation of virtual collaborative design environment (ViCoDE)—a new collaborative design system. ViCoDE features seamless integration of several immersive VR systems and a multitouch table that facilitates collaborative and interactive design work with immediate feedback. The evaluation of the system has been done during two real-life collaborative design workshops in a context of designing new healthcare environments. The results show that the VR system and multitouch table complement each other very well by facilitating both collaborative, and individual design spaces—and that it fosters better understanding, participation, communication, knowledge sharing, and collaboration among the different stakeholders. The contribution is two-fold: presentation of a new collaborative design system and the evaluation of its use in a real-life design context, which demonstrate how these new technology-based workshops may facilitate design management.

This research introduces a new scheduling problem called the resource-constrained project scheduling problem with combined precedence relations and resource calendars (RCPSP/CPRs-cal). Combined precedence relations (CPRs) extend generalized precedence relations (GPRs) and can effectively represent all possible combinations of precedence relations between activities. Resource calendars reflect resource time constraints that are closer to real-life project scheduling. A new network diagram was designed for RCPSP/CPRs-cal; it uses nodes to unify activities and precedence relations. It solves a problem in which traditional networks are prone to form cycles when representing combined precedence relations. An extended genetic algorithm (GA-NND) is proposed for solving RCPSP/CPRs-cal efficiently; it uses the new network diagram for coding to generate the node scheduling scheme. A three-step guideline and an example are provided for schedulers in order to help them better use GA-NND to solve RCPSP/CPRs-cal. Computational experiments were carried out to show that the extended genetic algorithm can solve RCPSP/CPRs-cal more quickly and accurately than the traditional genetic algorithm. The new problem contributes to developing a practical decision support system for resolving real-life constraints in projects.

Contractors attempt to manage optimal collection of human capital that can optimize the company’s performance. The skills and competency of human resources contribute considerably to the competitive advantage of the firm. Yet the skill shortage problem encourages utilizing strategies that bring more flexibility to workforce management. Multiskilling has been suggested as a potential strategy that assists workforce assignment across multiple projects. However, it brings new challenges for company workforce managers. This study contributes to the construction project management body of knowledge by proposing a simulation framework for assigning multiskilled, mainly managerial, workforces across multiple projects of one company. Meanwhile, it is a combination of a staffng and scheduling decision model, incorporating workers’ competency status. A cohesive mathematical model using the cost objective function optimizes the entire allocation process toward minimizing resource usage and fluctuations, minimizing costs, and maximizing social sustainability. Ultimately, a validation example incorporating diverse workers’ competency data is provided to exemplify how the optimization model performs in an academic setting compared with three commonly used methods in workforce assignment. This approach provides insight into simulating workforce assignments across multiple projects regarding competency.

Each year, thousands of new utilities are installed within the highway right-of-way (ROW) nationwide. This proliferation of utilities yields a great challenge to the traditional practice at road agencies to review, process, and track utility permit requests, document and manage utility conflicts, and check the regulatory compliance of newly installed utilities. This paper presents a new utility permitting system for road agencies, which is based on an object-oriented utility data model in Unified Modeling Language (UML) and its associated constraints in Object Constraint Language (OCL). The UML model defines the required information during the utility permitting process. The OCL constraints capture the regulatory requirements (mostly spatial constraints) that govern the installation of utilities within the ROW. A natural language processing (NLP) algorithm is designed to translate the requirements in textual documents into OCL expressions. The NLP algorithm achieves an average precision of 90% and a recall of 78%. The UML/OCL model is implemented in a spatial database to demonstrate its practical use in supporting automated utility permitting within the ROW. The novelty of this study includes (1) the capacity to model and store utility conflicts, including utility regulatory noncompliance, and (2) the incorporation of utility regulatory requirements as structured data for the automation of utility compliance checking. The newly developed system expedites the permitting process and contributes to the effective management of utilities within the ROW.

The construction industry’s traditional hoisting system always needs workers to complete the tasks involved, with concomitant extra labor costs and attention to the workers’ safety. This paper describes the development of an intelligent hoisting system to optimize the hoisting process, involving the application of robotic cars and vision-based recognition. In the design of the hoisting system, electric hooks were used and maneuvered by a robotic car while the vision-based recognition system—based on capturing images by the camera—arranges the robotic motion. The Yolo v2 recognition algorithm was used, which provides fast and efficient vision-based recognition. More than 30 trials in an experimental prefabrication factory indicated that the system had a significant success rate of approximately 92.5% (3.7/4)—the proportion of hooks successfully grappling the hoist points—verifying the feasibility of the system. The primary contribution of this paper is in the development and demonstration of an intelligent hoisting system to optimize the hoisting process, involving the application of robotic cars and vision-based recognition, thus furthering the application of computer vision techniques and robotics to construction work.

Risk factor identification is a critical topic in the construction industry. It is vital for the various construction firms and industry stakeholders to understand the different types of risks that affect their businesses and financial bottom lines. This research created a systematic methodology implementing a new set of text mining methods to identify and classify risk types affecting the publicly traded construction companies, by leveraging their 10-K reports filed with the Securities and Exchange Commission (SEC). A structured procedure was developed to apply advancements from text mining and natural language processing (NLP) to extract information from textual disclosures. A state-of-the-art deep learning algorithm named FastText was implemented to identify risk patterns and classify the text into appropriate risk types. Key findings showed that operational and financial risks associated with doing business most commonly are disclosed in the risk disclosures filed by the publicly traded construction firms. A steady monotonic increase was found in the average number of total risk disclosures from 2006 to 2018. Over the same period, growth occurred in the proportion of technology risks, reputation/intangible assets risks, financial markets risk, and third-party risks. The primary contributions of this research are (1) the development of a new methodology which serves as a risk thermometer for identification and quantification of risk at an individual company level, subindustry level, and the overall industry level; and (2) minimization of any existing information asymmetry in risk studies by utilization of a source of data that previously has not been used by construction researchers. It is anticipated that the developed methodology and its results can be used by (1) publicly traded construction companies to understand risks affecting themselves and their peers; and (2) surety bond companies and insurance providers to supplement their risk pricing models; and (3) equity investors and capital financial institutions to make more-informed risk-based decisions for their investments in the construction business.

Although complexity and risk are inherent characteristics of megaconstruction projects, existing project management approaches fail to incorporate complexity-based thinking into risk management. Complexity is usually considered as the source of risk events, along with uncertainty. However, prevailing risk management practices are oriented toward only handling the uncertainty. The lack of integration between the complexity and other risk-related concepts leads to unrealistic project risk assessments and the formulation of imperfect management strategies. The aim of this research is to explore the relationship between complexity and risk in detail and propose an integrated risk assessment approach. For this purpose, a mixed-methods research approach was adopted to collect data via semistructured interviews and survey questions. Data obtained from 11 megaconstruction projects carried out by Turkish contractors were analyzed in two ways. Quantitative findings served to verify the relationship between complexity and risk in numerical terms, whereas qualitative findings were utilized to develop a framework that explains the nature of this relationship. The conceptual framework represents the links between complexity and risk in megaconstruction projects, together with uncertainty and management strategies. The existence of causal relations between these concepts may constitute a significant challenge during the risk assessment. Therefore, an integrated risk assessment process (IRAP) was proposed so that megaproject practitioners could develop better risk management plans. The findings of this research were based on megaconstruction projects, but they may also apply to other types of projects.

Mixed reality (MR) has the potential to accelerate construction workforce training and enhance college education. However, there is a lack of empirical evidence in the research literature on how effective MR may be in facilitating student apprenticeship learning in field construction activities compared to the traditional paper-based approach. This research designed an outdoor wood-frame construction lab with two parallel sessions involving paper drawings (the control) and MR mockups (the experiment) for conveying design information. The research team used video recordings and questionnaires to collect behavioral and perception data for comparative analysis. The results indicated that student teams in the two sessions exhibited comparable construction productivity. However, they demonstrated different behavioral patterns and time allocation for technology use and reported different apprenticeship learning gains. This research also identified and discussed potential contributing factors that limited the success of MR. The contribution of this work resides in presenting empirical evidence of the impact of using MR on student apprenticeship learning through outdoor construction activities. It also provides peer educational researchers with valuable insights on how to study pedagogical use of MR with consideration of potential challenges that are present in realistic construction environments.

In the construction sector, the absence of a framework to incorporate and synchronize the resources required for activities means that projects risk incurring cost and time overruns. In this study, a framework was generated and tested to optimize resource allocation. The framework considers financial constraints through a genetic algorithm process and constraint-based simulation. The simulation method was developed based on the critical path method relation-defining system, with a dynamic prioritizing system automatically creating the simulation network. By introducing a standard cost database to the data management section of the framework, the total work required to insert data manually is minimized. For the majority of the activities, the user simply chooses an activity and determines its relationships with other activities. The rest of the initial required data are automatically pulled from the imported databases. The validity of the developed framework was demonstrated through a case study on constructing the structure of a residential building in Tehran, including four structural systems. The four systems were compared regarding time and cost of execution to illustrate the relevance of the framework. The framework decreases the user’s manual work while producing outputs of time, cost, and resources that are compatible with building information modeling (BIM) software. Overall, the result is an enhanced quality of project management plans. Moreover, the framework can be used as a planning tool at the preconstruction stage or as a control tool during the construction phase. The proposed approach provides a realistic project management plan, with the potential for substantial savings in finances and resources.

Selection of a project delivery method in highway construction is a challenging task because of the many decision criteria involved. In addition to quantitative project attributes, the project delivery decision-making process also typically relies on qualitative measures such as subjective judgments and experts’ opinions based on their experience with similar completed projects. Although current probabilistic methods provide a robust means to analyze quantitative data, they are not ideally suited for treating uncertainties encountered in qualitative data. To overcome the identified gap, this study investigated fuzzy pattern recognition, a mathematical technique based on fuzzy sets and fuzzy logic, to model a combination of quantitative and qualitative variables in highway project delivery selection. A fuzzy rule-based inference system was developed, trained, and tested using 254 empirical highway projects with particular project attributes including project type, project complexity, delivery risk, and cost performance. The proposed system was verified by performing a case project with the result of accurately recognizing the true project delivery method used and associated cost growth performance expectations. The flexibility of fuzzy membership functions in the proposed system helps leverage the evaluation of a combination of quantitative and qualitative variables in project delivery method selection in complex highway construction projects. In addition, this data-driven fuzzy inference system also allows for multiple decision scenarios based on the decision maker’s judgments of delivery risks and project complexity. This study contributes to the body of knowledge by developing an empirical inference system to recognize possible patterns of delivery methods associated with cost growths for new highway projects. This study may assist highway agencies in making project delivery decisions based on project attributes, historical data, and their relevant experience.

The behavior preference and decision matching of participants have an important influence on the governance of megaproject uncertainty. However, the framework and drivers largely are unknown, and are little studied. This study used regulatory focus theory to describe the motivation orientation of all parties. In addition, this study discussed the relationship between regulatory focus and megaproject uncertainty through the choice of governance mechanism—trust and control. A partial least-squares structural equation model was used to test the hypothesis based on data collected from 219 respondents. Results showed that promotion and prevention focuses have negative effects on megaproject uncertainty. Promotion focus has a positive effect on trust but does not have a significant effect on control. Prevention focus has a positive effect on control and a negative effect on trust. Trust mediates the relationship between promotion focus and megaproject uncertainty, whereas control does not. Trust and control mediate the relationship between prevention focus and megaproject uncertainty, make rational behavioral decisions in different uncertain situations, and choose matching governance modes. Furthermore, regulatory focus theory based on megaprojects goes beyond the boundaries of a single organization. This theory occurs at an interorganizational level and helps to build a governance mechanism for megaproject uncertainty. Results of this study enrich the knowledge system of megaproject management theory.

Because of the complexity of construction projects, the use of building information modeling (BIM) has become a more efficient solution. The main problems with conventional methods, such as the critical path method and program evaluation and review technique, are the lack of precision in predicting time due to the dynamics of the construction process. One solution is to combine planning models with discrete event simulation (DES) and BIM to simulate the dynamic environment to reduce uncertainties. Likewise, DES is used to implement just in time (JIT). A problem with JIT implementation using DES in the construction industry is a lack of accurate measurement of the optimum time for each process. Meanwhile, takt time, one of the main techniques in JIT, was used in this research to achieve the optimal time of each process and was combined with DES to solve this problem. First, a 4D BIM model was developed to extract data. Then, takt time was used to achieve optimal time for each activity, and eventually, a DES was performed. The results of this study indicate that the proposed framework using takt time and its combination with DES to integrate BIM with JIT will lead to realistic and optimal planning.

Thousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition–based smart training assistant system that can automatically evaluate workers’ performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition–based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs.

Energy models should be simplified to handle data limitations and should predict reliable energy use. Currently, it remains challenging to ensure an appropriate level of detail for simplifying building energy models and to avoid performance gaps when predicting electricity consumption. In this respect, this research proposes to identify an appropriate level of simplifying a building energy model, predict electricity demands and performance gaps using the simplified energy model, and expand the model usability through the operational stage. Building electricity demands predicted through EnergyPlus (version 8.7.0) simulation are compared with actual electricity data collected through Internet of Things (IoT) sensors. Consideration of performance gaps increases the predictability of electricity consumption of a simplified energy model. Also, the Bayesian multilevel additive model updates the performance gaps along with the collection of new IoT data. The findings of this study contribute to forecasting electricity demands with a simplified energy model by predicting performance gaps that can be applied to predicting the electricity needs of similar buildings in the design stage and controlling operational electricity use in the operational stage by comparing sensor measurement with reference data provided by the energy model.

Research examining underpricing in social infrastructure projects has been limited (e.g., schools), yet this is a reality that confronts many governments worldwide. The upshot of underpricing is often contractual claims and delays, which can harm the social and economic well-being of citizens. Cognizant of experiencing potential cost overruns, this paper demonstrates that agencies within the Brazilian public sector institutionalized the winner’s curse to ensure their infrastructure projects did not exceed their budgets. This action resulted in contractors abandoning works before they were completed and, in doing so, being subjected to contractual sanctions. For the government agencies, this led to increased costs because an alternative contractor was required to complete works. The decision to accept a low bid, therefore, had the opposite of the desired effect because the final project costs exceeded their budgets. It is proposed that government agencies introduce an irresponsible bid threshold, which acts as a benchmark for determining a bid’s disqualification to address the winner’s curse phenomenon.

The commercial and legal challenges of offsite construction technology have become more pronounced with the ongoing rise and growing popularity of such construction method. Offsite construction represents a combination between construction services-based activities and manufacturing/fabrication goods-based tasks, thus called hybrid transactions. This mixed nature has created many uncertainties concerning whether the common law or the Uniform Commercial Code (UCC) shall govern. To this end, this paper aims to enhance the knowledge of the commercial and legal treatment of hybrid offsite construction transactions. To achieve that, this paper followed a research methodology that employs three types of analysis: analysis of the commercial and legal provisions, case study analysis, and comparative analysis. That said, the paper (1) studied the UCC’s commercial and legal stipulations with regard to hybrid offsite construction transactions; (2) analyzed six case studies in the US to examine the decision structure upon which the courts determine whether the common law or the UCC shall govern the hybrid offsite construction transactions; and (3) compared between the common law and UCC provisions as related to the treatment of hybrid offsite construction transactions. To verify the usefulness and benefits of the performed analyses in this paper, the authors shared the outcomes of the paper with a focus group of leading attorneys and experts at two major US law firms with an average experience of more than $20\mathrm{years}$ in hybrid transactions, offsite construction processes, and their related commercial and legal considerations. This paper provides guidance on different commercial and legal aspects of hybrid offsite construction transactions including offer acceptance and withdrawal, requirements for a written contract, offsite-fabricated goods’ inspection and rejection, risk of loss, and warranties, among others. Further, eight factors were identified to determine whether the common law or the UCC shall govern hybrid offsite construction transactions. This paper makes many contributions to the body of knowledge, with the primary contributions being providing a better discernment of the commercial and legal aspects of hybrid offsite construction projects and offering guidance to the management, commercial, and legal practitioners on different considerations related to offsite construction transactions.