Journal of Construction Engineering and Management

As prefabrication of timber structures in the construction sector becomes part of the solution to overcome the housing and environmental crisis, the lack of efficiency of this industry needs to be addressed. This inefficiency, characterized by low production capacity, high costs, and large workforce, are in part due to the low adoption of automation and robotic technologies, caused by insufficient knowledge and research measuring the impact of these technologies. This paper aims to measure the efficiency of introducing automation in a prefabrication environment of timber-frame structures, through a decision analysis tool. Four layout types with varying automation levels are explored using simulation modeling, while KPIs are exploited to measure the efficiency of each production environment. A sensitivity analysis is conducted to assess the most performing layout if a new type of wall, requiring fewer tasks, is introduced in the product mix. A design of experiments (DOE) is also performed on the fourth layout, a fixed robotic cell with the highest level of automation, to assess the most influential variables. Results show a positive increase in worker, space, and production efficiency as the level of automation increase. This research contributes to the body of knowledge by providing a decision analysis tool supporting the choice of an optimized layout for the prefabrication of timber-frame walls (TFW). The findings and the decision analysis tool should provide solid grounds for future exploration of automation and practice in the field.

Although leader-focused interventions to improve safety performances on construction sites have been widely proposed and discussed, the mechanism linking informal safety communication between leaders and workers is poorly known. This research developed and tested a theoretical model regarding the effects of leader–member exchange on the informal safety communication of construction workers, focusing on the cognitive psychology perspective. Psychological and cognitive factors were examined as mediators in this research. Meanwhile, empirical data were collected from 333 construction workers in China using a detailed questionnaire. Structural equation modeling was implemented to analyze the data using commercially available software. The results showed that frequent exchanges with frontline supervisors could make workers more willing to communicate safety issues with coworkers. There were differences in how supervisors activated the three types of safety communication. The psychological contract of safety had more substantial mediating effects on the relationship between leader–member exchange and the self-needed safety communication of workers. In contrast, psychological safety was more related to citizenship safety communication than participatory safety communication. These results enrich the theoretical knowledge of leader–worker research and provide practical insights for promoting worker interactions beneficial to safety performance onsite.

Green buildings have gained increasing momentum during the past decade, evidenced by an uptick in green building construction worldwide. To facilitate green building development, it is crucial to understand the current geographical distribution of green buildings, thereby identifying emerging markets and opportunities for future growth. However, very few studies investigated the spatial characteristics of the green building market and the factors influencing the distribution of green buildings. By examining the national green building market in the United States, we studied the spatial distribution patterns and significant influencing factors of the county-level US green building markets. Cluster mapping and hot spots of the green building markets were identified through spatial autocorrelation analysis. A spatial regression model with high performance ($R^2$ of 0.83) was developed to identify the significant influencing factors. We found a statistically significant clustering phenomenon of county-level US green building markets. The spatial error model reveals that three factors, namely, the number of housing units, gross domestic product, and the local green building company index, significantly influence the number of green buildings at the county level. We also found that the county-level green building market is influenced by these factors of not only the host county, but also the neighboring counties. Our findings provide a useful reference for stakeholders’ decision-making process concerning local green building market development.

Thousands of aging education buildings in the US are in urgent need of maintenance to ensure their operational performance. The planning and scheduling of these maintenance activities need to be optimized to minimize their total maintenance costs and reduce the challenging deferred maintenance problems confronting education institutions in the US. This paper presents the development of a novel model for optimizing the planning of maintenance activities in education buildings. The computations of the developed model are implemented utilizing an optimization module that identifies an optimal maintenance schedule for minimizing total maintenance costs; a scheduling module that calculates the start day, start hour, finish day, and finish hour of all maintenance activities; and a cost estimating module that estimates the total maintenance cost for each generated schedule. An application example of three education buildings that include plumbing, HVAC, and electrical maintenance activities was analyzed to illustrate the use of the model and evaluate its performance by comparing its results to those generated by widely used existing methods for planning maintenance activities of education buildings. The outcome of this analysis confirms the model was capable of outperforming existing methods in reducing the total maintenance cost by more than 12%. The main contributions of the developed novel model to the body of knowledge include its innovative methodology for identifying optimal ranking of maintenance activities, selecting optimal overtime use and crew size for all maintenance activities, generating an optimal maintenance schedule that complies with all practical constraints such as the availability of classrooms during their nonoperational hours, and minimizing total maintenance cost of all scheduled activities.

The construction sector accounts for a significant proportion of natural resource consumption and waste generation. This reveals the essentiality for gravitating the operations in the industry toward more sustainable paradigms. To tackle these concerns, the circular economy (CE) model has become a central concept to render conventional production and consumption behaviors in construction projects into innovative and sustainable patterns. In construction projects, selecting the most competent contractor is of paramount importance. Hence, the present research seeks to establish a comprehensive evaluation framework for sustainable circular contractor selection based on a hybrid fuzzy multicriteria decision-making (MCDM) approach. In this respect, the fuzzy analytical hierarchy process (AHP) was adopted for assessing the CE indicators, while the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was utilized for evaluating circularity and eligibility of contractors. The utility of the proposed framework was predicated with regard to hydropower projects due to several environmental challenges encountered in the corresponding subsector. The results show that the contractors can be circular only if they have strong financial viability, develop strategies to implement ReSOLVE (regenerate, share, optimize, loop, virtualize, exchange), adopt specific construction methods to CE (e.g., modular construction), and propose sustainable innovative solutions. Overall, the proposed hybrid fuzzy MCDM framework can be used as a more systematic and transparent approach for selecting the most circular and sustainable contractors, contributing to the preservation of earth’s resources. Given the current contractors’ limited capacity to address circularity and sustainability concerns, the findings of this study can be regarded as a roadmap and contributes to practice by achieving circular and sustainable construction objectives with waste reduction, cost savings, and environmental benefits.

Asset management of pavement network requires understanding of pavement deterioration rate for cost-effective maintenance and adequate budget allocation. The pavement industry has recognized the challenge of uncertainty or variation in deterioration processes that could not be captured by deterministic deterioration models. This study investigated the stochastic Markov chain theory for modeling deterioration of pavement network. The discrete condition data for the Markov model is obtained by a proposed maintenance-related condition rating scheme (MRCR) that combines three commonly inspected pavement distresses including cracking, rutting and roughness. The Markov model is calibrated by the proven Bayesian Markov chain Monte Carlo simulation method, and the statistical Chi-square test is used for testing model fitness. A case study with time series data of pavement distresses collected from regular inspection of a highway network is used in this study. Various influential factors to pavement deterioration are also investigated in this study to understand their impact on the deterioration rate of highways. The results on the case study show that the Markov model is suitable for modeling deterioration of highway network, and there are significant differences in deterioration rates of highways among influential factors including traffic volume, rainfall amount, demographic location, and prioritized maintenance. The outcomes of this study provide more understanding of pavement deterioration of road networks and demonstrate the forecasting of maintenance budget by the deterioration prediction of Markov model for supporting asset management of pavement network.

In shield tunnel boring machine (TBM) tunnel construction, grouted pea gravel is used to fill gaps between the surrounding rock and segment rings, acting as a connecting layer that significantly impacts the support performance of the segment ring. This study first obtained the mechanical parameters of pea gravel through experiments, followed by an in-situ investigation to analyze the stress and surface settlement law of the segment ring during TBM construction. A discrete-continuous coupling model was then employed to examine the load transfer process at the micro level between the surrounding rock, pea gravel, and segment ring. Finally, physical model tests were conducted to compare the stress characteristics of the segment ring under different states of pea gravel during continuous loading. The findings reveal that loose pea gravel fails to sufficiently transfer the surrounding rock load to the segment ring, resulting in inadequate support and continuous surface settling until the pea gravel is consolidated. The degree of compression of loose pea gravel directly affects the contact force acting on the segment ring and its supporting effect on the surrounding rock. Moreover, the ultimate bearing capacity of the segment decreases when the pea gravel is loose. The state change of pea gravel resulting from grouting plays a crucial role in the supporting effect of the segment ring; thus, simultaneous grouting is necessary during tunnel construction in unstable strata or insufficient pea gravel filling.

Real-time assessment of the ergonomic risks to which workers are exposed at a workstation and the provision of real-time corrective feedback intervention to workers play an essential role in improving safety in the workplace through the reduction of long-term exposure of workers to the ergonomically hazardous postures associated with physical fatigue and work-related musculoskeletal disorders. This study proposes a framework, virtual reality–based real-time ergonomics training (VR-RET), that integrates virtual reality (VR) and an inertia motion capture system to rapidly assess postures, providing the following inputs in real time: (1) full-body postural ergonomic risk assessment that deploys existing rule-based methods such as rapid upper limb assessment (RULA) and rapid entire body assessment (REBA); (2) auditory feedback, triggered when the exposure to ergonomic risks is higher than a predefined threshold; and (3) visual feedback intervention to correct ergonomically hazardous postures through the provision of recommendations during training on industrialized construction tasks. The proposed framework is verified through a pretest/posttest procedure in conjunction with a randomized control group experiment involving 37 subjects. Based on the comparison of the pretest and posttest data, a reduction of 35% in the percentage of time spent being subjected to ergonomic risks in the high-risk range is observed when training is administered using VR-RET and RULA is deployed as the risk assessment method; in contrast, a significant reduction is not observed when rapid entire body assessment is used. This study’s contributions are twofold: (1) a framework for providing ergonomic and operational training through VR simulation based on real-time acquisition and processing of body motion data (with the objective of mitigating worker behaviors that increase exposure to the ergonomically hazardous postures that can lead to a work-related musculoskeletal disorder); and (2) updated evaluation of the effectiveness of real-time RULA and REBA assessments integrated with real-time auditory and visual postural feedback intervention for ergonomic risk reduction.

The construction industry contributes significantly to global environmental loads with massive amounts of construction and demolition waste (CDW) ending up in landfills. To address the need for efficient CDW management, this research proposes a new decision support framework for managing construction waste generated during end-of-life activities for building projects. The framework monetizes potential environmental savings from different recovery options (e.g., reuse, recycle, and so on) and uses multiobjective optimization to determine the optimal quantity of material to undergo each material recovery scenario. The framework uses parametric weights to consider stakeholders’ preferences and their appreciation of environmental benefits compared with costs. A case study of a renovation project in Waterloo, Ontario, Canada, is used to demonstrate how the proposed framework can divert concrete and glass waste from the landfill. For this particular project, savings of 200 GJ of embodied energy, ${22\mathrm{m}}^3$ of water, and over 12 t of greenhouse gases can be realized from optimal recovery planning using the proposed framework. This study concludes that decision support systems should be used well in advance of end-of-life activities to evaluate trade-offs for recovery planning activities effectively.

Limited skilled labor has been one of the greatest challenges facing the construction industry. The COVID-19 pandemic has further exaggerated the already strained construction labor market, leading to an additional negative impact. One of the major contributors to skilled labor shortages in construction is the issue of labor retention. Overall, this is a complex and dynamic situation that requires effective and efficient simulation-based techniques to capture the interdependent relationships that affect the performance of the construction labor market. This paper fills this knowledge gap. To this end, the authors used a multistep research methodology that involved (1) identifying factors that affect skilled labor shortages; (2) developing a one-module system dynamics model that consists of three interconnected systems (namely, construction labor market system, industry characteristics system, and economic conditions system); (3) initializing and calibrating the model to simulate the construction labor market; (4) validating the model through structural, behavioral, and calibration tests; and (5) conducting sensitivity analysis to simulate different parameters and examine their impact on skilled labor shortage. Among other findings, results indicated that all scenarios were successful in improving the conditions of the skilled labor market by increasing the workforce size and labor retention rate. Further, the model demonstrated that economic indicators have a more impactful influence on labor retention patterns compared with industry characteristics. The developed model offers industry practitioners, policymakers, business analysts, and other associated stakeholders a useful tool to test various scenarios including national-level economic policies and labor retention regulations that affect the construction skilled labor market. Consequently, this allows users to analyze the impact of variables such as fiscal policies, economic support plans, and construction spending strategies.

Abstract

In multipartner projects, team members from two or more organizations often have different skills and behavioral preferences. A major purpose of managing such teams is fostering cooperation among different types of team members, which is critical for the successful completion of a project. Traditional studies assumed that the preferences of members within a team are homogeneous and examined their effects on cooperation. Motivated by the heterogeneity of team members’ preferences within a team, we propose a model of effort expenditures in multipartner project teams with two heterogeneous members to explore how the cooperative effort expenditure (CEE) of one agent is affected by the preference type of a coworker. We theoretically formulate the problem, provide the solution, and conduct the parameter sensitivity analysis. Furthermore, we present a numerical example to demonstrate the usage of the model and gain insight into the mechanism of the effect of heterogeneous preferences on the team cooperation. The theoretical analysis and experimental results showed that the effort cost and potential value of collaboration are the most critical factors that determine the interaction among the team member’s optimal CEE, project outputs, and the coworker’s preference type. The payoff differences between team members play a complex moderating role in the effect of team members’ heterogeneity preference types on team cooperation. This paper contributes to the body of knowledge on project management by understanding the cooperative behavior of team members with heterogeneous behavioral preferences, and providing managerial insights for project managers to promote cooperation in multipartner project teams.

Practical Applications

Multipartner projects have become increasingly common due to the increasing complexity and difficulty of the projects. These projects, such as mega construction projects, require the input from professionals of diverse disciplines. But the heterogeneities of partners (stakeholders) increase the difficulty of cooperation. This paper provides managerial insights and suggestions for the project managers to promote cooperation between team members with heterogeneous preference types and marginal effort contributions in multipartner projects. First, for low-complexity projects (such as simple housing construction projects) and high-complexity projects (such as the Hong Kong–Zhuhai–Macao Bridge project), the project managers may pay less attention to the heterogeneity of team members’ behavioral preferences. Second, for other moderate-complexity projects, project managers should emphasize soft management to reduce the negative influence caused by behavioral preference heterogeneity, such as hiring team members with high risk tolerance, reducing payoff information transparency between team members and concealing their payoffs, fostering reciprocity among the agents, reducing their level of spitefulness through team building, and reducing competition, as well as encouraging the positive emotions (such as altruism) through publicity, education, praise, and rewards. Third, to avoid negative influence caused by profit-sharing differences (PSDs), it is better for project managers to set a higher profit-sharing rate for team members with a higher marginal contribution.

Abstract

A method for construction process planning is presented together with a visualization and computing support tool named ViWoLZo-2. The method is based on the work density construct that depicts the time a trade contractor (trade) needs to complete a certain scope of work, location by location. The method helps planners explore alternative process durations by allowing them to divide the work space into zones to increase concurrency in their plan and smooth the workflow. ViWoLZo-2 can be configured for any site plan and process step sequence. The program computes work density, which is then used for workload leveling and zoning, and depicts metrics to gauge the quality of the plan. Although work density data may not yet be readily available, they can be collected and used in ViWoLZo-2 to support a planner’s what-if analyses and replace more subjective approaches for creating plan concurrency. The paper concludes with recommendations for tool enhancements and suggestions for follow-on research. The contribution to knowledge is the design and implementation of a novel tool for construction process planning.

Practical Applications

When planning construction processes, teams need to decide who will do what work, where, and in how much time. Rather than having them use intuition without a formal rationale or data, the novel ViWoLZo-2 tool presented here provides them with a procedural, data-driven approach to decide. ViWoLZo-2 uses the concept of work density that depicts the time a trade needs to complete a certain scope of work in a given area. Using the tool’s visualizations and metrics, planners can reduce workload variability and increase concurrency in their plan to ensure it will meet schedule milestone dates. They can explore alternatives and make informed decisions on trades to include in a process, on sizing of crews, on delimiting zones where trades will work, and on the time trades will get to complete their work within a zone. The tool can be configured to any work area, process, and type of work. It can handle repetitive work but is equally helpful when repetition of work is not easily recognizable. ViWoLZo-2 supports planning methods (such as takt planning) that aim to achieve plan reliability.

Due to the substantial economic and social benefits brought about by the timely completion of airport projects, it is important to accurately estimate construction duration and set reasonable project schedule goals during the project planning phase. However, the uncertainties and complexities associated with terminal construction projects limit the applicability of traditional duration estimation methods. Case-based reasoning (CBR) has been applied to estimate the construction duration for complex projects with sparse information in the early project stage, but a few of the detailed CBR procedures warrant further investigation for enhancing the estimation performance. Therefore, a CBR model with improved case retrieval and case retention strategies was established to enhance its performance in predicting the duration of airport terminal construction. After identifying the key factors influencing construction duration, the CBR models were developed with 48 terminal construction projects in China according to the following four steps: (1) retrieve cases by using three weight assignment methods and five similarity measurement methods, (2) reuse the solutions of the retrieved cases, (3) revise the solutions by performing multiple regression analysis (MRA), and (4) retain the new cases based on the case retention strategy. The performance of the CBR models was then evaluated by using 12 test cases and comparing the resulting absolute percentage error (APE) and standard deviation (SD) values. The results verified that the genetic algorithm (GA)-based CBR models using gray correlation, Euclidean distance, and fractional function to calculate similarities outperformed their model counterparts, and they confirmed the usefulness of the proposed case retention strategy for improving estimation accuracy. This research contributes to the improvement and expansion of CBR-based duration estimation models and provides a valuable auxiliary tool for practitioners to make more rational decisions during the schedule planning and control of airport construction projects.