Journal of Construction Engineering and Management

With increasing implementation of off-site prefabrication and modular construction technologies, the complexity of material supply chain management on construction projects has grown substantially. However, research on construction scheduling has yet to take dynamic material logistics as an explicit constraint in analytically deriving construction schedules and addressing impacts of uncertainties in material supply on project budget. This study proposes a two-step analytical approach to tackle the identified problem. First, a constraint programming-based scheduling optimization model is developed to derive project schedules subject to variable material delivery times and finite crew resource availability. The second step is to take advantage of the valid optimization model for evaluating the impact of different input settings of material logistics on project budget. An example project adapted from the literature is used to illustrate the effectiveness of the proposed optimization model in coping with variable material delivery times. Based on the same case, the delivery date of a particular material is singled out as the risk factor of interest in order to derive the complex relationship between material delivery date and total project cost. In addition, a case study based on a bridge girder fabrication project is presented to demonstrate the applicability of the proposed optimization model on projects of practical size. In conclusion, this study adds to the body of knowledge by developing an analytical methodology that factors material supply constraints into the resource-constrained scheduling optimization model so as to analyze the impact of uncertainties in material deliveries on project budget.

Construction workers’ emotional states (e.g., pleasure, displeasure, excitement, and relaxation) are known as a critical factor that affect their performance (e.g., safety, health, and productivity). To prevent adverse impacts on work performance, measuring emotional states should take precedence to better understand how workers’ emotions vary while they are working. Among many methods available to measure emotional states, electroencephalogram (EEG) has a great potential for quantitative measurement by overcoming a possible bias from the survey-based subjective assessment of emotions. Although EEG-based emotion measurement has been tested and applied only in a laboratory environment, recent advancements in wearable EEG sensors, which are portable, wireless, and affordable, open a new door toward nonintrusive field emotion measurement. This study thus investigates the feasibility of measuring workers’ emotions in the field using a wearable EEG sensor. To do this, a bipolar dimensional emotion model, which consists of valence (from displeasure to pleasure) and arousal (from relaxation to excitement) dimensions, was applied to quantify workers’ emotional states. Then, workers’ valence and arousal levels were measured using a wearable EEG sensor during their ongoing tasks. The validity of the EEG-based emotion measurement was examined through a comparison with cortisol levels obtained from workers’ saliva samples, which has been accepted as a reliable physical measure of emotions. The results demonstrate the applicability of a wearable EEG sensor for measuring workers’ emotions, particularly valence levels, which remain crucial to understanding workers’ emotional states. This study contributes to the body of knowledge on in-depth studies for understanding workers’ emotions in the field by providing a means to continuously and nonintrusively measure workers’ emotions while they are working.

This paper presents the development of a novel optimization model for the scheduling of repetitive construction projects. The model provides original and unique capabilities that enable planners to search for and identify an optimal/near optimal schedule that simultaneously minimizes project duration, crew work interruptions, and interruption costs. The model computations are organized in four major modules: (1) an optimization module that searches for and identifies a near optimal schedule that simultaneously minimizes the project duration, work interruptions, and interruption costs; (2) an initial scheduling module that calculates the early start date and work-continuity total float for each activity in all of its repetitive sections; (3) an intermediate scheduling module that generates a set of feasible schedules that simultaneously minimize project duration and work interruptions; and (4) an interruption cost module that calculates total interruption cost for each of the generated schedules. An application example of a repetitive construction project is analyzed to evaluate model performance and demonstrate its capabilities in optimizing the scheduling of repetitive construction projects.

The digital tools and practices that facilitate the collection, organization, and use of accurate data and information throughout the life cycle of a highway infrastructure asset are referred to as civil integrated management (CIM). The collective impact of CIM practices and agency policies on project and asset performance has yet to be established by data-driven research. This paper empirically models the effect of CIM practices on the resulting reduction in change orders, savings in the schedule, improvements in quality of work, and safety benefits. The modeling framework in this study also incorporates suitable constructs to study the influence of agency approaches such as financial resources, team alignment, information management policies, standards, and contract specifications. Through case studies, CIM implementation and performance data were compiled in detail across 12 highway projects in the United States and a megaproject in the United Kingdom. Cross-case comparisons were then carried out using qualitative comparative analysis to extract the causal conditions for outcome measures. Results indicated the presence of multiple solution pathways for explaining performance benefits. The solution pathways that include the CIM attribute sufficiently explained the reported performance benefits for the projects. However, lower necessity scores of this attribute (below 0.6) showed that CIM as an enabling ingredient is not always necessary. As such, alternate solutions that exclude CIM in causal pathways do exist and they reiterate that technology is just a supportive tool. Information management strategies and contract standards and specifications recorded high sufficiency and necessity scores (above 0.8), indicating their significance to performance. The findings from this empirical research underscore the multidimensional nature of CIM implementation, ascertain the associated agency factors, and demonstrate a novel performance framework for agencies investing in the integration of digital practices.

This paper develops a hybrid optimization approach combining genetic algorithm (GA) and integer linear programming (ILP) to solve the nonlinear optimization problem of managing the fuel consumption and emissions of a tracked bulldozer. Furthermore, the authors propose that a continuously variable transmission (CVT) can better exploit the efficient zones of the engine maps. The original transmission system of the Caterpillar D6T bulldozer consists of a five-gear transmission, whereas the gear ratios of the proposed CVT are continuous and can be assigned according to transmission design. The fuel consumption and three emission items of the engine, unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx), are studied. Vehicle–terrain interactions are formulated and the excavation program is characterized by excavation depth and speed. The target of the multiobjective optimization problem is a combination of fuel rate and three emission items. Results show that, for digging depths less than the bulldozer blade maximum digging depth, the target can be improved by more than 31% using CVT incorporated with GA compared to the conventional transmission, obtained by shifting engine operating points from low efficiency zones to optimum points. Finally, integer linear programming is used in a hybrid manner with GA to solve for the optimum combination of excavation steps in tasks of specified digging depths more than the maximum digging depth of the bulldozer blade. Results show that the proposed method can improve the target value up to 18% with the same digging time, and can improve the target value up to 32% using the hybrid optimization approach without time constraint.

Evaluating the safety maturity of construction contractors before awarding the contract is an effective strategy that many owners have started to implement. Given the importance of workplace safety, awarding the contract to safer contractors can enhance project performance outcomes in terms of safety, quality, cost, and schedule. Safety maturity is one quality of a construction organization encompassing interdependent factors that can be used to evaluate expected safety performance. However, there is no formal decision-making framework available in literature that owners can use to evaluate contractor safety maturity. The present study aims to bridge this gap in safety knowledge by proposing a decision-making framework that can be used to evaluate the safety maturity of construction contractors. Development of the decision-making framework included two tasks. First, an integrative literature review to identify influential safety maturity factors and their potential indicators was performed. The result of the review revealed seven factors (safety leading indicators, safety lagging indicators, safety and supervisory personnel, system maturity and resiliency, preconstruction services, technology and innovation, and safety culture) that influence the safety maturity of construction contractors. Second, the identified factors, and their indicators, were integrated into a formal multicriteria decision-making method, referred to as Choosing by Advantages, to evaluate the safety maturity of five construction contractors on a selected case study project. The proposed framework is expected to provide practical and theoretical directions on how to evaluate contractor safety maturity using relevant evaluation factors and sound decision-making methods.

Standard scheduling tools for construction projects with repetitive tasks assume that labor productivity remains constant throughout the project lifetime. None of these tools accommodate the dynamics of learning throughout the project. This paper introduces a tool that uses nonlinear optimization to integrate learning curve concepts into task duration estimates for construction project scheduling. The tool, featuring a graphical user interface, mines past data to select the most appropriate learning model from a suite of existing models. Testing the tool on data obtained from five published case studies with varying sizes and locations suggests that the tool offers accurate estimates for task completion times, even when the size or quality of the input data is minimal. Directives for use of this tool and an estimate of potential savings in practice are also provided through an example based on real-world data. These savings amounted to 28% of the overall labor costs within the real-world project. The contribution of this paper is a tool to estimate construction task durations in such a way that learning is incorporated. The tool uses nonlinear optimization to select and calibrate the best learning model making the tool of value to practitioners working across a variety of linear and nonlinear repetitive projects in a range of geographical regions.

Workspace in construction projects is considered a resource and a constraint that needs to be addressed in project scheduling. Without proper workspace planning, time–space conflicts frequently may occur at construction sites. In this study, a novel four-dimensional building information modeling (4D-BIM) dynamic conflict detection and quantification system was developed to identify time–space conflicts and quantify their impacts on project performance. Using the proposed approach, the conflict between different activities workspaces was identified considering the labor crew movement in the assigned workspace during different time intervals. For this purpose, four distinct execution patterns were defined, which in combination with four starting positions, led to 16 execution alternatives. The conflict’s severity was then calculated through the quantitative assessment of its effects on labor productivity. To evaluate the performance of the proposed approach, it was implemented on a building project. The time–space conflicts were detected and quantified using the proposed approach. Finally, a what-if analysis was performed to select the optimum execution scenario with the minimum number of time–space conflicts. Using the proposed approach, conflict detection can be performed more accurately, and a more precise value for conflict severity is achieved.

The North American construction industry began to experience a shortage of skilled labor in the 1980s, which has continued as a repetitive cyclic trend over the last three decades. Although this issue has received significant attention from researchers, less attention has been given to quantifying the impact of skilled labor availability on project performance. This study contributes to the body of knowledge by filling a gap in the existing literature by quantitatively modeling and elucidating the influence of skilled labor availability on construction project cost performance. Data from 97 construction projects completed in the United States and Canada were collected from two industry databases. The primary analysis shows that projects that experienced craft shortages underwent significantly higher growth in cost overruns compared with projects that did not. Further analysis on both databases returned two robust multiple regression models that demonstrate similar patterns of the risk that a skilled labor shortage poses to cost performance. By using data-combining techniques, two data sets were combined to obtain more reliable informative models. The primary benefit of this risk estimation is to enable project management teams to determine whether mitigation strategies are warranted to prevent potential shortfalls in project cost performance. Understanding the level of impact that craft shortages have on project performance can serve as a primary step in developing motivation for industry leaders, communities, and construction stakeholders to address the challenge of skilled labor shortage in construction industry.

Decision-support tools for infrastructure planning assume that the real cost of construction (i.e., the cost of construction when adjusting for inflation) will remain constant over the life cycle of a facility. This paper is the first of its kind to evaluate the validity of this assumption by assessing the long-run (i.e., multidecade) nature of construction costs. This study begins by testing for the possibility that Baumol’s cost disease, a phenomenon found in some industries in which labor compensation growth outpaces productivity gains, thus giving way to real cost growth, afflicts the construction sector. To do so, a series of regression models are developed using historical macroeconomic data from the US Bureau of Economic Analysis on construction costs, compensation, productivity, and the price of intermediate and capital goods. Because construction cost growth is also closely tied to price changes for inputs, this research extracts long-run real price trends of important intermediate goods used in construction through time-series methods applied to publicly available data from the US Bureau of Labor Statistics and the US Geological Survey. The results of this study provide strong empirical evidence that Baumol’s cost disease is present within the construction sector, whereas the real price of most construction commodities has not exhibited a negative nor positive secular trend over the last century. These two findings suggest that, contrary to the conventional assumption found in current analytical frameworks, the real cost of construction will rise in the long run. This study’s contribution should motivate decision-makers to re-examine their existing decision-support tools, because the value of policies that reduce project completion times and increase the service life of facilities is potentially much higher than currently anticipated.

Engineering project networks (EPNs), as digitally mediated units whose team members are scattered across different organizations/locations, have become the norm for construction project teams. In EPNs, virtuality captures various forms of boundaries among team members and influences major dimensions of team functioning and outcomes. Despite this importance, investigating the role of virtuality on the functional performance of EPNs has remained an unexplored area. Through a multidisciplinary review of literature, a theoretical model has been created to conceptualize the fabric of links between virtuality and effectiveness in EPNs. The theoretical basis of the model is the Big Five theory of team effectiveness. The model has been evaluated using empirical data collected from 285 completed questionnaires, and analyzed through the use of partial least squares structural equation modeling (PLS-SEM) technique. Quantification of associations reveals that virtuality significantly affects effectiveness in teams by manipulating several mediators. However, the level of influence of virtuality on these mediators, and eventually effectiveness, is much lower than previously assumed by current literature. This study goes beyond existing studies, presenting the first quantified model of virtuality applicable to any type of EPN in the construction context.

The risk of major occupational accidents involving tripping hazards is commonly underestimated with a large number of studies having been conducted to better understand variables that affect situation awareness: the ability to detect, perceive, and comprehend constantly evolving surroundings. An important property that affects situation awareness is the limited capacity of the attentional system. To maintain situation awareness while exposed to tripping hazards, a worker needs to obtain feedforward information about hazards, detect immediate tripping hazards, and visually scan surroundings for any potential environmental hazards. Despite the importance of situation awareness, its relationship with attention remains unknown in the construction industry. To fill this theoretical knowledge gap, this study examines differences in attentional allocation between workers with low and high situation awareness levels while exposed to tripping hazards in a real construction site. Participants were exposed to tripping hazards on a real jobsite while walking along a path in the presence of other workers. Situation awareness was measured using the situation awareness rating technique, and subjects’ eye movements were tracked as direct measures of attention via a wearable mobile eye tracker. Investigating the attentional distribution of subjects by examining fixation-count heat maps and scan paths revealed that as workers with higher situation awareness walked, they periodically looked down and scanned ahead to remain fully aware of the environment and its associated hazards. Furthermore, this study quantitatively compared the differences between the eye-tracking metrics of worker with different situation awareness levels (low versus high) using permutation simulation. The results of the statistical analysis indicate that subjects did not allocate their attention equally to all hazardous areas of interest, and these differences in attentional distribution were modulated by the workers’ level of situation awareness. This study advances theory by presenting one of the first attempts to use mobile eye-tracking technology to examine the role of cognitive processes (i.e., attention) in human error (i.e., failure to identify a hazard) and occupational accidents.

Computer vision–based tracking methods are used to track construction resources for productivity and safety purposes. This type of tracking requires that targets be accurately matched across multiple camera views to obtain a three-dimensional (3D) trajectory out of two or more two-dimensional (2D) trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions, and workers in greatly similar high-visibility apparel. This paper proposes a novel vision-based method that addresses all these issues. It uses as input the output of a 2D vision-based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second uses geometric restrictions, whereas the third correlates color intensity values. The proposed method features a promising performance of 97% precision, 98% recall, and 95% accuracy.

Delivering building information modeling (BIM) requires a certain level of trust to make it more effective. However, the engineering, procurement, and construction (EPC) approach, which conventionally adopts transaction law, encourages each party to protect their legitimate interest. Consequently, EPC contracting parties operate within their own goals and procedures, in which trust is not a fundamental contracting strategy. In addition, distrust—which is commonly perceived as detrimental to the relationship—should receive more attention when examining the beneficial outcomes of the relationship. Contract research has emerged from focusing on the safeguarding of contract transactions and is currently moving toward coordination and contingency adaptability for its success. In this context, this paper proposes an integrative trust-based functional contracting model that describes how trust can enhance BIM performance in EPC projects. Thus, this paper contributes new knowledge of the proper use and harmonization of contract functions and provides significant insight for the construction industry to think beyond the traditional EPC contract setting for effective use of BIM.

A positive safety culture is crucial for improving the safety performance in construction workplaces. Although there are substantial efforts to achieve a high level of safety culture, the traditional approaches may not be sufficient to ensure the sustained improvement of the safety performance due to the complex nature of construction projects. This technical note aims to discuss the concept of resilient safety culture and its application in the construction environment. A conceptual model for a resilient safety culture was developed on the basis of the safety culture theory, the resilience engineering theory, the latent failure model, the human error theory, the high reliability theory, and the normal accident theory. The conceptual model proposes a measurement method for the resilient safety culture of construction projects and explains how a resilient safety culture can be created in a construction organization. This paper also proposes hypotheses regarding the effect of the project complexity and resilient safety culture on the safety performance of construction projects, on the basis of the normal accident theory. The model may serve as the theoretical basis for establishing an ultrasafe construction organization that is expected to achieve a consistently high safety performance regardless of the changing complexity levels and conditions of its construction projects.