Journal of Construction Engineering and Management

Modern communication technology has had a profound influence on the way people work, often resulting in a blurring of the boundaries between work-life and family-life environments through after-hours, work-related contact. Previous studies have explored the associations between job autonomy and control, job pressure, work contact, work–family conflict, psychological distress, and sleep problems. These studies used a variety of measurement scales based on items drawn from the extant literature, but limited psychometric evidence surrounding the use and interpretation of scores derived from these scales has been provided. Given the pivotal role that the measurement of occupational health psychology constructs plays in improving the understanding of occupational health and well-being, this study extends their work by employing multidimensional item response theory (IRT), which has been absent in its application within construction management. Thus, IRT was used to gain empirical insights into the psychometric validity of these scales among construction professionals in South Africa ($N=864$). Importantly, the IRT analyses show that the scales provided the most precision when used with construction professionals with lower levels of job autonomy and control and job pressure, moderate levels of work contact and work–family conflict, and moderate to high levels of psychological distress and sleep problems. The results extend beyond the classical analyses emanating from previous studies by shedding light on specific item- and scale-level properties. In a redesign, especially for job autonomy and control and job pressure, the addition of more items could reduce the level of measurement error across the continuum on each respective scale. Better measurement of these occupational health constructs will enhance understanding of the well-being of employees and organizations and facilitate appropriate interventions to improve the well-being of construction professionals.

Knowledge management practices in architecture, engineering, and construction (AEC) projects emphasize team integration and motivational factors to improve knowledge transfer among team members, many of whom come from different disciplines and organizations. However, increased knowledge transfer might raise transaction costs without bringing desired benefits if team members are not able to properly process transferred knowledge. To contribute to knowledge management practices in AEC projects, this study aims to identify key individual-level knowledge-related factors and develop indicators to measure them. Based on the literature, we identified four factors, namely knowledge application, absorptive capacity, articulating capacity, and common knowledge, and developed a set of indicators to measure them. To test the validity and reliability of these indicators, we collected data from an AEC project team including more than 160 individuals. Confirmatory factor analysis and Cronbach’s alphas supported the validity and reliability of the indicators. The study’s contribution to the body of knowledge is the expansion of our understanding of knowledge management practices in complex multiteam systems by considering and measuring individuals’ knowledge-related factors in AEC project teams.

Construction engineering and management are vital for successful project execution, and both researchers and practitioners continually seek ways to improve construction processes. Fuzzy logic plays an important role in many construction engineering and management applications, which are reviewed in this paper. This paper discusses the limitations of fuzzy logic and how this theory has been combined with other modeling techniques to develop fuzzy hybrid techniques, and describes the aspects of construction problems and decision making that are most effectively modeled using these techniques. Fuzzy hybrid techniques that are most common in construction are presented and examples from construction literature and the author’s research program are provided. The author shares her vision of future research in this area, which is based on her expertise and experiences collaborating with construction industry partners, who have helped shape her research program and its impact on industry. Finally, the author presents her thoughts on the challenges construction researchers face in translating research to practice and measuring its impact, and she discusses some potential solutions from her research program. This paper is based on the 2019 Peurifoy Construction Advancement Address, which the author presented in Montreal, Canada, in June 2019.

This study aimed to investigate the internal relationships between safety investments and construction employees’ behavioral performance with safety cognition as the mediating factor. A comprehensive methodology was adopted, including theoretical modeling of safety investments, survey questionnaire, and structural equation modeling (SEM). In the theoretical model, four factors were used as safety investment categories: personal protection equipment (PPE), safety education, insurance purchased for site employees, and safety incentives. These four categories were studied for their correlation to the overall safety investment, which was tested for its contribution to employees’ behavioral safety performance in both direct and indirect ways. Indirectly, safety cognition was introduced as a mediator to bridge safety investments and behavioral performance. A questionnaire consisting of 28 indicators was adopted to describe safety investment, safety cognition, and behavioral performance. A random sampling approach and the top-down method were implemented to recruit construction site employees from the southeastern region of China. The followup SEM analysis revealed that all four investment categories positively contributed to the overall safety investment, which was found significantly correlated to employees’ safety cognition and behavioral performance. Safety incentive was identified as the most significant factor contributing to the overall investment. The current study extends prior studies of safety investments by adopting a quantitative approach from the employees’ perspective. Insights are offered to construction employers on how safety investments can affect behavioral performance—for example, the importance of balancing the tangible (e.g., incentive) and intangible (e.g., safety insurance) investment categories. This study also contributes to establishing the internal links among safety investments, safety cognition, and behavioral safety performance. Based on the current findings, future work could investigate how to optimize safety investments to achieve better behavioral performance. The current study, based in China, could be applied in a different geographic context by testing the correlations between safety investments and behavioral safety performance.

Knowledge management is considered to be a critical organizational capacity to create competitive advantage for construction organizations. This study used the knowledge governance perspective to propose that project planning can be unitized to improve knowledge integration in construction projects and, in turn, to achieve project success. The study quantitatively evaluated the relations between project planning, knowledge integration, and project success using partial least squares structural equation modeling with survey data from 301 project members in the Chinese construction industry. The results indicate that project planning can indeed improve knowledge integration and, in turn, facilitate construction project success in which knowledge integration plays a mediating role. The study contributes to filling the gap in understanding how project planning can be conductive to construction project success from the view of knowledge integration. It also provides new theoretical guidance for utilizing project planning to enhance knowledge integration in construction organizations rather than the traditional way of establishing a complex knowledge management system.

Incentive contracts are widely used in construction to not only provide motivation but also to develop a cooperative relationship among project players. However, establishing the best outcome sharing is seen as a challenge to those involved in contracts design. Although the optimization of incentive contracts has been approached by a number of researchers, there is no guidance on optimum incentive contracts with multiagent/multioutcome arrangements. This paper develops the optimum multiagent/multioutcome sharing model in incentive contracts with a risk-neutral owner and risk-averse agents (contractors, consultants). Optimization, principal–agent theory, and utility theory provide the basis for the development. The application of the model is addressed in different scenarios. The paper demonstrates that adopting optimum effort by agents is an effective strategy that avoids project time overruns and also gives maximum savings to all contracting parties. It is shown that: at the optimum, outcome (time, cost, quality, and safety) sharing is positively related to an agent’s contribution and negatively related to the agent’s effort cost; as the correlation of the outcomes or the level of agent risk aversion increases, agents prefer low outcome sharing; and outcome variances and outcome sharing are related negatively which means that high outcome variances result in low outcome sharing. The contribution of this study lies in providing insights into designing optimum incentive contracts with multiagent/multioutcome arrangements.

A vast amount of information is produced in each phase of major construction projects. However, extensive project lifespans and complex organizational relationships may contribute to significant information loss, attenuation, and separation during the transmission process. Consequently, some important information (i.e., regarding costs, schedules, materials, facilities, properties, etc.) may be suspended or interrupted when being passed to corresponding organizations; this seriously affects project management performance. One effective solution is to increase the amount of information sharing (IS). This study was aimed at exploring the mechanism of how organization change (OC) and process operation capability (POC) influence IS in major construction projects from the perspective of resource dependence theory (RDT). The relationships between IS, OC, and POC were examined using 444 questionnaires collected from managers working in major construction projects across Jiangsu, China. On this basis, indicators to measure OC, POC, and IS in the context of major construction projects were established. In addition, the effects of strategies that improve IS from OC and POC were quantified by combining the structural equation model (SEM) with a fuzzy cognitive map (FCM). The results showed that (1) both OC and POC have a positive impact on IS, and POC serves to mediate the influence of OC; (2) the indicators for measuring IS, OC, and POC demonstrate good reliability and validity; and (3) with mediation of POC, the effects of improvements to IS can be doubled when OC is increased, relative to implementing POC alone. This research contributes to the IS literature by demonstrating and quantifying the positive effects of OC and POC on IS from the perspective of RDT. Moreover, the identified indicators provide major construction projects with a promising tool for assessing IS, OC, and POC. Practical strategies for improving IS and managerial implications of the indicators are also discussed in the context of major construction projects.

Deteriorated water distribution networks require significant investments to maximize their functionality. The problem is that limited financial resources are allocated for rehabilitation strategies. This deficiency highlights the importance of developing a tool that helps decision makers develop maintenance and replacement management plans. The optimization tool is employed using two evolutionary algorithms: genetic algorithms and particle swarm optimization. The efficacy of the developed model is demonstrated through its application in a case study of Shaker Al-Bahery, Egypt. Furthermore, evaluation metrics are considered to compare the performance of the aforementioned algorithms. The results reveal that the particle swarm optimization exhibited superior results when compared with the genetic algorithms. Moreover, the following two multicriteria decision-making techniques are used to provide a ranking for the near-optimum solutions: multiobjective optimization on the basis of ratio analysis and technique for order preference by similarity to ideal solution. Finally, the Spearman correlation coefficient is utilized to assess the correlation between rankings obtained from different decision-making methods. The results indicate a very strong relation among the aforementioned techniques.

Over the past two decades, the construction industry has encountered the challenge of rapid aging of craft workers in many countries. In addition, the shortage of skilled workers that has emerged over the past three decades in various countries have forced project managers, at least in some regions or periods of time, to execute projects with less skilled or experienced workers. There has been increasing attention from researchers and practitioners on fatal injury preventive solutions. However, the industry still suffers from a relatively high and substantial disproportion in fatal injuries compared to other industries. This study contributes to the body of knowledge by elucidating the fatality rate fluctuations in the different age groups of craft workers and empirically examining the interaction of adverse impact of higher age and preventive influence of higher educational attainment and skill and experience level of craft workers on fatal injuries in construction projects. A total of 6,326 accidents were analyzed, which occurred in construction projects in the province of Tehran, Iran between 2011 and 2017. The study found the higher experience and educational attainment of craft workers significantly mitigates the severity of accidents from fatal injuries to nonfatal ones in young and middle-aged craft workers. However, the effectiveness of both factors decreases gradually when craft workers get older. Both factors’ strength will be dominated by the adverse impact of age on the older workers, which results in a high fatality rate between the age of 65 and 75.

Tower-crane accidents occur frequently and often have serious consequences. To identify the critical causes of tower-crane accidents, this study adopted a method that combined system thinking and case analysis. The entirety of tower-crane accident causes was considered as a system, and was divided into six subsystems and 34 causal factors. Information was collected on 141 investigation reports of tower-crane accidents in China from 2013 to 2018. The occurrence frequencies of accident causal factors and situation severities of subsystems were statistically analyzed. A combination of order relationship analysis and decision-making trial and evaluation laboratory methods was used to quantitatively evaluate the correlations among different causal factors, and the results of a comprehensive centrality and causality of 34 causal factors were obtained. Critical causes of tower-crane accidents were identified, including workers’ wrong actions, inadequate safety training, inadequate safety inspection, low safety awareness, and safety engineer’s mismanagement. To verify the practicability of the system model and identified critical causes, a case study of a typical serious tower-crane accident was conducted. Finally, corresponding recommendations are proposed for tower-crane safety management improvement and accident prevention.

New forms of data science, including machine learning and data analytics, are enabled by machine-readable information but are not widely deployed in construction. A qualitative study of information flow in three projects using building information modeling (BIM) in the late design and construction phase is used to identify the challenges of codification that limit the application of data science. Despite substantial efforts to codify information with common data environment (CDE) platforms to structure and transfer digital information within and between teams, participants work across multiple media in both structured and unstructured ways. Challenges of codification identified in this paper relate to software usage (interoperability, information loss during conversion, multiple modelling techniques), information sharing (unstructured information sharing, drawing and file based sharing, document control bottlenecks, lack of process change), and construction process information (loss of constraints and low level of detail). This paper contributes to the current understanding of data science in construction by articulating the codification challenges and their implications for data quality dimensions, such as accuracy, completeness, accessibility, consistency, timeliness, and provenance. It concludes with practical implications for developing and using machine-readable information and directions for research to extract insight from data and support future automation.

Construction accidents can negatively affect the performance of a project. Evaluating the financial consequences of these accidents is an effective method for measuring their impacts on owners, contractors, and society. While several studies have estimated the cost of injuries among different trades, demographics, event types, injury sources, and nature of injuries, most of these studies presented descriptive statistics without analyzing any inferential statistics regarding the differences between average costs. To address this limitation, this study utilized robust measures of location (trimmed mean) and scale (Winsorized variance) in a three-way factorial ANOVA design to examine the effect of variables on the cost of injuries among electrical contractors. To create a reliable accident database of electrical contractor injuries, the team aggregated 388 nonfatal accidents collected from the 2016 Occupational Safety and Health Administration (OSHA) database. To determine the effectiveness of inferential statistical methods as applied to injury-cost analysis, null hypotheses were tested using three robust methods: (1) the Welch-type procedure; (2) an extension of Yuen’s method; and (3) percentile bootstrapping. The results of the study confirm that robust hypothesis testing approaches can be successfully implemented on safety data even when the assumptions of conventional test statistics are violated. In terms of the electrical case study, the outcomes indicate that various event types and project end-uses can change the cost of injuries among electrical contractors. Specifically, the findings revealed that caught in/between and exposure to electricity accidents can, on average, lead to higher injury costs than fall to lower levels and struck-by objects/equipment accidents. In terms of project’s end-use, construction accidents, on average, resulted in costlier injuries in nonbuilding projects than in buildings. The methodological results of this study can help contractors better quantify the risks of a construction project/task by estimating the severity of potential accidents in monetary values. Furthermore, this study contributes to the current body of safety knowledge by assessing alternative methods of hypothesis testing that do not require specific assumptions.

A lack of clear-cut contractual safety responsibility is one of the main problems in construction safety. Therefore, it is in the interest of all project parties to assign proper contractual safety responsibilities before the execution of the contract. Despite the increased popularity of design–build (DB) contracts, there is a scarcity of research work that addresses DB contracts in general and the contractual safety aspects in particular. This research study fills this critical knowledge gap by providing contractual safety guidelines in relation to the parties’ rights and responsibilities and the tort liability/negligence under national DB standard forms of contract. To achieve that, the authors used a multistep methodology. First, the authors analyzed and compared the safety-related provisions as stipulated by national DB standard forms of contracts. Second, the authors presented law case studies to provide practical perspectives on the different aspects of the safety-related provisions and their relation to tort liability/negligence. Third, the authors developed the advantages and disadvantages for each of the studied contracts. Fourth, the authors developed a checklist to enable contract administrators to avoid poor or unclear contract language and to better draft new contracts in terms of safety-related issues. The outcomes of this research work will enable contract administrators to clearly assign safety responsibilities between the different parties of the project. Clear assignment of safety responsibilities in contracts will enhance the overall safety performance of projects and minimize claims, conflicts, and disputes related to safety injuries in the construction industry.

Off-site construction aims to shift construction work to a factory environment, thereby enabling automation of the wood framing process. Current automated or semiautomated solutions for wood framing are assisted by a series of support and buffer tables that occupy a large footprint within industrialized construction plants. As the wood panels become longer to minimize transportation costs, so do their necessary support systems. To provide a more cost-effective solution for wood framing support systems, this paper proposes a simulation-driven redesign. The simulation model was built on existing data from current machinery, enabling users to modify design parameters and machine logic without incurring costs associated with prototyping. The simulation results were then analyzed from performance and cost perspectives to decide on the final design parameters. The redesign of the wood framing support systems was found to be almost 40% less costly and more than 10% more productive than currently available systems, showcasing the importance of simulation within the field of construction automation.

Labor productivity is a major determinant of project performance in construction. Models of labor productivity in construction tend to focus on learning curve theories that assume learning is an individual process with no transfer of knowledge among crew members. This paper seeks to extend theories of individual learning to capture the crew dynamics present on construction sites. Accordingly, this paper presents an agent-based model aimed at deriving the impacts of crew composition and project schedule on knowledge sharing and, thus, on task duration. The proposed model was calibrated using field observations of 201 interactions among 12 construction workers at a construction project in Beirut, Lebanon. The results indicate that more diverse crews witness higher levels of knowledge sharing and greater productivity gains. The results also suggest that schedules keeping all the workers busy eliminate the potential for knowledge sharing and thus only benefit from the baseline gains seen in individual learning. This work contributes to the literature by developing an agent-based model that simulates knowledge sharing in the construction industry at the worker level. The study is limited by its exclusion of multiskilled workers.

Overexertion-related construction activities are identified as a leading cause of work-related musculoskeletal disorders (WMSDs) among construction workers. However, few studies have focused on the automated recognition of overexertion-related construction workers’ activities as well as assessing ergonomic risk levels, which may help to minimize WMSDs. Therefore, this study examined the feasibility of using acceleration and foot plantar pressure distribution data captured by a wearable insole pressure system for automated recognition of overexertion-related construction workers’ activities and for assessing ergonomic risk levels. The proposed approach was tested by simulating overexertion-related construction activities in a laboratory setting. The classification accuracy of five types of supervised machine learning classifiers was evaluated with different window sizes to investigate classification performance and further estimate physical intensity, activity duration, and frequency information. Cross-validation results showed that the Random Forest classifier with a 2.56-s window size achieved the best classification accuracy of 98.3% and a sensitivity of more than 95.8% for each category of activities using the best features of combined data set. Furthermore, the estimation of corresponding ergonomic risk levels was within the same level of risk. The findings may help to develop a noninvasive wearable insole pressure system for the continuous monitoring and automated activity recognition, which could assist researchers and safety managers in identifying and assessing overexertion-related construction activities for minimizing the development of WMSDs’ risks among construction workers.

Many construction accidents are caused by workers’ unsafe behavior, and evidence suggests that individuals who are not sensitive to risks are accident-prone. Therefore, identifying workers with certain personal characteristics related to habitual insensitive response to safety risks potentially can help construction managers develop individualized safety training and interventions. Although the methods by which construction managers could identify those workers with specific personal characteristics have been limited, wearable sensors provide an opportunity for construction managers to monitor individual workers’ behavior without any interruptions. This study investigated whether and how sensing data about workers’ behavior around hazards can reveal their personality traits related to risk-taking behavior. In particular, this research focused on a personality trait well known to be related to risk-taking behavior, locus of control (LOC). An experiment was designed to collect workers’ behavioral response to various hazards in terms of gait patterns; LOC was measured using standard instruments. The analysis showed that the degree of gait change around hazards is significantly correlated with LOC score. Additionally, those with internal LOC had more consistent gait patterns over a number of repetitive exposures to the same hazard (i.e., sustained sensitivity to safety risks) than those with an external LOC. These results demonstrate the potential of using wearable sensors to identify workers with personality traits associated with unsafe behavior.

Augmented and virtual reality have the potential to provide a step-change in productivity in the construction sector; however, the level of adoption is very low. This paper presents a systematic study of the factors that limit and drive adoption in a construction sector–specific context. A mixed research method was employed, combining qualitative and quantitative data collection and analysis. Eight focus groups with 54 experts and an online questionnaire were conducted. Forty-two limiting and driving factors were identified and ranked. Principal component analysis was conducted to group the identified factors into a smaller number of factors based on correlations. Four types of limiting factors and four types of driving factors were identified. The main limitation of adoption is that AR and VR technologies are regarded as expensive and immature technologies that are not suitable for engineering and construction. The main drivers are that AR and VR enable improvements in project delivery and provision of new and better services. This study provides valuable insights to stakeholders to devise actions that mitigate the limiting factors and that boost the driving factors. This is one of the first systematic studies to present a detailed analysis of the factors that limit and drive adoption of AR and VR in the construction industry. The main contribution of this study is that it grouped and characterized myriad limiting and driving factors into easily understandable categories, so that the limiting factors can be effectively mitigated and the driving factors potentiated. A roadmap with specific short-term and medium-term actions for improving adoption was outlined.

Cell-based path planning is studied and applied worldwide. Building information models (BIMs) can be used as the environment for path planning. However, studies providing an applicable and detailed approach to converting a BIM to cells are rarely reported. This study proposes a voxel-based method that can automatically convert a BIM to cells. This technique, verified for accuracy and applicability, can quickly and conveniently create a cell-based path-planning environment based on a given BIM before testing and applying path-planning methods. With this method, researchers and practitioners can focus more on research and the application of path planning instead of the creation of a cell-based environment, which is time-consuming and tedious.

Optimal allocation of risk between the public and private sector is instrumental to the enhancement of value-for-money (VFM) for public-private partnership (PPP) infrastructure projects. This study offers a novel method that considers the risk mitigation capability (RMC) of the public and private sector to evaluate VFM of a PPP infrastructure project under the performance-based annuity (PPP/AP) scheme. It is the first attempt to incorporate both the RMC and risk allocation decisions into VFM development quantitatively. This model applies a combination of the Takagi-Sugeno fuzzy inference system and statistical methods. The perceptional data from Indonesian PPP experts were collected to estimate model parameters and assess the VFM of a 73 km Indonesian public road, and then used to illustrate the application of the model. The result shows that moderate levels of RMC for both the public and private sector, worsened by suboptimal risk allocation, lead to a no-go decision with PPP/AP. The proposed model makes earlier PPP decision-making possible for the public sector before invitations for bids.

Past studies indicate that the US construction industry is facing challenges to attract young talent to pursue craft careers. The main goal of this research is to understand the underlying factors that influence career selection of the next generation of craft workers in order to help the industry overcome those challenges. The authors designed a survey based on the Theory of Planned Behavior. The survey was distributed across the US among individuals between the ages of 15–24 who were exposed to Career Technical Education (CTE) or participated in construction training programs. A total of 778 completed questionnaires was received. The statistical analysis shows that having work experience in construction-related jobs can significantly improve intention to choose a career in the industry. There is also a strong correlation between self-confidence and intention. A multivariate regression model is created to understand the relative importance of components of attitude towards working in construction. The results of this study helped the researchers make several recommendations on how to attract the next generation of craft workers to the industry.

Building information modeling (BIM) produces positive results for the construction delivery process. BIM obtains these types of results by enabling information interoperability in respect of a building or facility throughout its life cycle phases starting at the conceptual design, continuing to facility management, and, in particular, dealing with contractual challenges for BIM-based construction projects. In spite of these compelling contributions by BIM in optimizing construction processes, their complexity leaves them still vulnerable to risk and uncertainty from unforeseen circumstances. The case study presented in this paper has developed insights into the implications of enhancing information interoperability by assessing the following categories of BIM-based contractual issues: (1) technology compatibility, (2) auditing procedures, (3) responsibilities, information and communication technology (ICT) protocols, and processes, and (4) transfer procedures. Project documents were reviewed followed by semistructured interviews with eight relevant project stakeholders to enable data triangulation. The set of results obtained was based on adopting the hierarchical Jaccard’s coefficient cluster analysis that then initiated a starting point from which a more comprehensive data interpretation was developed. Several new codes emerged from the session. Respondents described BIM as a new strategic management innovation, and they discussed the need to consider legal aspects when devising BIM contracts. The strategies were then consolidated into a model data validation conceptual framework as a means of protecting the data from loss, corruption, and/or manipulation.