Journal of Construction Engineering and Management

This study examined logistics planning in the modular integrated construction supply chain (MiC-SC). Existing models overlook two important characteristics of the MiC-SC—the dynamic behavior of its stakeholders, and the uncertain interactions between its activities and resources—making their applicability limited in real practice. The literature also lacks an understanding of which logistics and construction decisions significantly impact the MiC-SC key performance indicators (KPIs) (e.g., project duration, MiC-SC costs, and emissions). Moreover, optimizing logistics and construction decisions to achieve a trade-off between these KPIs is yet to be addressed. This study contributes to the MiC-SC knowledge by (1) developing a multimethod simulation model that captures the dynamic behavior of MiC-SC stakeholders through agent-based modeling and the uncertain interactions between their internal activities and resources using discrete-event simulation and considering more realistic features such as traffic condition, site layout, heterogeneous truck types, and uncertainty of activities duration; (2) integrating the developed model with the Taguchi approach (TA) to identify the significant logistics and construction decisions that impact five KPIs (project duration, construction costs, construction emissions, logistics costs, and logistics emissions); and (3) applying a simulation-optimization (SO) approach to find the near-optimum logistics and construction decisions that maximize the MiC-SC sustainability through minimizing the project duration, total costs, and carbon emissions. The model’s applicability was demonstrated using a case study of a high-rise MiC project. The TA analysis highlighted some interactions between logistics and construction decisions, calling for collaborative decision-making between stakeholders. The proposed SO approach could provide them with near-optimum decisions that minimize the project duration, MiC-SC costs, and emissions.

Worksite accidents in the construction and infrastructure industries result in cost overruns and schedule downtimes. Previous research has analyzed cost and schedule deviations caused by worksite accidents within these industries. However, a quantitative analysis of accidents at the subsector level and comparisons at the organizational level have yet to be undertaken. Toward this aim, worksite accident data are collected across different industry subsectors and organizations of different sizes, and an integrated risk register is constructed to capture cost and schedule deviations caused by worksite accidents. The findings indicate that structural and installation services are the main contributors to workplace accidents in terms of cost and time deviations. Moreover, organizational size is an important factor in the impact analysis of worksite accidents. Large organizations in the structural services were found to make a significant impact on cost overruns and schedule downtimes. They are followed by small organizations in the installation services subsector and medium/large organizations in the heavy and civil engineering subsector. The findings in this study reveal that improvements and investments in safety training and workplace communications are needed for efficient accident prevention.

Mobile information and communication technologies (MICTs) have been widely adopted to facilitate construction project management by many construction organizations. Yet, scholars have rarely emphasized the growing concern regarding the overuse issue of MICT among construction management professionals, especially construction project managers (CPMs). Against this background, drawing on the stressor-strain-outcome framework and job demand-resources model, this research aimed to explore how different MICT overuse patterns (i.e., MICT excessive use at work and MICT connective use after work) by CPMs affect their well-being in terms of job burnout and the underlying mechanism. The moderating role of team member support was also examined as the boundary condition in the influencing process of MICT overuse. Empirical data from 216 CPMs were analyzed with structural equation modeling. The results revealed that both patterns of MICT overuse led to the two strains including technology–work conflict and role overload, which in turn exacerbated CPMs’ job burnout. In addition, team member support was found to buffer the impacts of MICT connective use after work on these strains. This study highlights the need for a deeper understanding of the negative side of MICT use in the construction project context with guidance for construction organizations on mitigating the adverse effects of MICT use in practice. Furthermore, these empirical findings bring useful insights into the causes of CPMs’ job burnout through a novel human–technology interaction perspective.

Building information modeling (BIM) can integrate multi-stage resources and promote multi-agent collaboration. As a key information technology in the architecture, engineering and construction field, BIM has attracted increasing attention worldwide. Existing research mainly focuses on influencing factors or project-based applications of BIM, lacking a macroscopic and quantitative measurement. This study quantitatively explores the macro characteristics and dynamics of BIM adoption. A total of 4,591 BIM-related patents were collected, and the current BIM adoption status was clarified. The evolution process of BIM adoption from 2011 to 2020 was revealed using social network analysis, and the development trend was predicted based on a Barrat, Barthelemy, Vespignani (BBV) model. The results indicate that BIM adoption in China presents significant regional imbalances and scale-free network features, with low connectivity as a whole and a significant island effect locally, and is entering a stage of rapid development and active evolution. This study provides an overview of actual BIM adoption and its potential causes, which is a breakthrough in existing research. The conclusions will contribute to the government issuing evidence-based policies to promote BIM at large.

Collaboration tightly relates to the effective management of construction projects. Especially for building information modeling (BIM)-enabled construction projects (BECPs), collaboration is the prerequisite for effective BIM use. However, different from traditional construction projects, the collaboration process in BECPs should transform into an integrated mode to achieve BIM benefits. Specifically, partners should adopt effective collaborative behaviors supporting an integrated collaboration mode. Therefore, this study intends to systematically identify those effective collaborative behaviors in BECPs. The Integrated project delivery (IPD) philosophy was taken as the theoretical lens, and the grounded theory approach was utilized to deduce the collaborative behaviors based on semistructured interviews about 34 BECPs. Five categories of collaborative behaviors aligned with the IPD philosophy for integrated collaboration were identified, including “Adaption to customized BIM use,” “Integrated coordination,” “Integrated communication,” “Shared responsibility to reduce conflicts,” and “Flexibility to support the team.” Meanwhile, a theoretical model was established that described how these collaborative behaviors combined for effective BIM use. This research helps broaden the understanding of how partners should collaborate in BECPs and provides a model for further empirical examination.

Construction projects involve multiple company departments and disciplines. The departments follow certain rules in implementing a project, also referred to as requirements in a construction contract. Current administration practices do not show which discipline or department is related to any requirement in the contracts. Thus, all departments need to review contract requirements but typically only from their perspective and with minimal communication with one another. In addition to the tendency of this manual process to error, time and money are lost in evaluating irrelevant departmental requirements. This study concentrates on one aspect of contract interpretation, coordination of the contract requirement review. Automating a classification of the contract requirements by relevant departments can increase the efficiency of contract reviews. This study proposes a robust approach to automating contract sentence classification by relevance to the company department. The approach comprises both natural language processing (NLP) and supervised machine learning techniques to train an algorithm. Training data are selected from an internationally and widely used standard form of construction contract. Precision metric results as high as 0.952 and recall metric results as high as 0.786 are acquired by support vector classifiers (SVCs). These are considered sufficient within the context of multilabel classification of construction contract sentences for construction professionals to operate without further training. The developed methodology reduces time spent on contract review, reliably and accurately predicts classification of contract sentences for departmental relevance, and also removes the dependence on expert participation in coordination efforts contract review.

Although slump testing has been used extensively to assess the workability of conditioned soil in shield-driven tunnelling, this traditional evaluation method still requires many subjective descriptions. This study developed a novel and objective index to assess the workability of conditioned coarse-grained soil. A series of slump tests was performed on gravely sands with different conditioning parameters. The test results suggested that foam conditioning was suitable only for sands (lacking fine grains) with low water contents. Inspired by the observation that appropriately conditioned soils usually form a platform with a certain diameter on the top of slumped specimens, a novel evaluation index is proposed to evaluate the workability of conditioned coarse-grained soil. In contrast to the traditional method of using slump value to assess conditioned coarse-grained soil, a prominent advantage of this method is that it eliminates the need to judge whether water or foam bleeds from slumped specimens, and thus it is easier to implement on construction sites. The results of verifications with theoretical analysis and field applications on a construction site confirmed that this new method is practically effective for evaluating the conditioning state of coarse-grained soil in mechanized tunnelling with earth pressure balance (EPB) shield machines. The proposed $F$ index provides a simple but quantitative evaluation perspective for the workability of conditioned soils, and thus is of significant engineering value.

Both contractual and relational governances affect building information modeling (BIM) collaboration and implementation (BCI) to improve project performance. However, the mechanism by which these governances and their interaction influence BCI remains undetermined. Considering transaction cost economics and social exchange theories, this study aimed to investigate the extent of the effect of contractual and relational governances and their interactions on improving BCI. We developed a series of hypotheses and a questionnaire to achieve the aims of the study. The partial least-squares structural equation modeling was applied to analyze the cause–effect relationship arising from the hypotheses. The results show contractual and relational governances affect BCI positively. Contractual governance, which consists of monitoring elements in safeguarding provisions, positively affect BCI and project performance. However, there is no effect found for safeguarding provisions that define the rights of parties in the contract. The study also reveals that contractual governance does not influence relational governance in BCI. This research provides new insights to project managers on the use of effective governance mechanisms to deliver BIM-enabled megaprojects for improving project performance.

Bridge owners possess important data sources, such as bridge construction records and inspection and maintenance reports, which hold great promise for improving understanding of bridge deterioration and informing maintenance decision making. However, the valuable data buried in these documents are not being fully exploited due to their unstructured nature. Domain-specific semantics are needed to facilitate analysis of the data based on content and domain-specific meaning and to bridge terminology gaps across different sources. To address this need, this paper proposes a bridge ontology (BridgeOnto) that captures deterioration knowledge and semantics related to bridge elements, deficiencies, deficiency causes, and maintenance actions. The proposed ontology was evaluated through answering competency questions, automated consistency and redundancy checking, expert interviews, and application-oriented validation. The BridgeOnto was implemented in supporting automated extraction of information describing bridge conditions and maintenance actions from bridge inspection reports. The experimental results show that the ontology was able to improve information extraction precision, recall, and F-1 measure by 11.7%, 12.4%, and 12.0%, on average. This research contributes to the body of knowledge by offering an ontology that can capture the key deterioration knowledge areas with sufficient coverage and in-depth classification for achieving adequate support for bridge document analytics. By allowing better access to and use of crucial textual data, this research has the potential to improve the understanding of bridge deterioration and enhance maintenance decisions.

In prefabricated construction (PC) projects, component manufacturing and site construction are implemented concurrently to improve productivity. However, concurrent PC provides feedback information on each task, and iterations and coupling tasks often exist, which causes rework at a certain probability. Therefore, it is necessary to identify and optimize coupling tasks to reduce feedback information and rework costs and time. In this paper, a PC process optimization model that includes two modules is constructed. The first module is a coupling task optimization module and is built using the design structure matrix (DSM) method with graph theory to identify and reduce coupling tasks. Then a rework risk impact optimization module is built with cost and time minimization as objectives based on a genetic algorithm (GA) according to risk communication, rework probability, and core tasks. A case study is used to verify the model and reveals an 82.24% reduction of the impacts of rework risk on costs and time. The coupling tasks that are optimized as task groups require that the corresponding stakeholders be coordinated. Core tasks should be carried out in advance to avoid greater impacts in later periods. The model extends existing process optimization methods and has value for planning and controlling PC management to enhance coordination among all stakeholders. This study provides a reference for future research on rework risk prediction.

By maximizing offsite work and minimizing onsite activities, modular construction has become increasingly popular in high-rise, high-density cities. For successfully delivering high-rise modular buildings, a key issue is the effective scheduling of offsite logistics, consisting of module manufacturing, transportation, and storage. This paper proposes an integrated offsite logistics scheduling approach for high-rise modular building projects that addresses project-specific constraints and uncertainties, based on the stochastic Petri net (SPN) method. The proposed approach supports multi-criteria decision-making, that considers completion time, total cost, storage capacity utilization, and production process stability under what-if scenarios. Its efficacy and novelty for offsite logistics scheduling were demonstrated through a real-life project case study. The results indicate the need to (1) strengthen suppliers’ demand responsiveness by shortening module production time; (2) enhance manufacturing flexibility by increasing factory storage utilization; (3) improve supply chain synchronization; and (4) capitalize on the government’s role in offsite logistics enhancement. The findings methodologically innovate offsite logistics scheduling, and practically support contractors and module suppliers in successfully delivering high-rise modular building projects in high-density cities.

This study investigated the moderating effect of personality traits in the association between worker characteristics (work experience, training, and previous injury exposure) and hazard-identification performance through mechanisms of visual attentional indicators. Through an integrated moderated mediation model, the attentional distribution, search strategy, and hazard-identification performance of participants were examined across 115 fall hazards. Results indicate that individuals with more work experience and safety training were better at hazard identification independent of visual attention and regardless of personality. Furthermore, individual differences in conscientiousness and openness personality dimensions significantly moderated the associations between (1) worker characteristics and visual attention; and (2) visual attention and hazard identification. This study provides empirical evidence for the potentially pivotal role of worker characteristics and dispositional traits with regard to hazard-identification performance on jobsites. These findings can empower safety managers to identify at-risk workers and design personalized intervention strategies to improve the hazard-identification skills of workers.

Prefabrication and modular construction is promoted globally as a sustainable and efficient construction method. The greenhouse gas (GHG) emissions from prefabricated and modular construction have been widely studied and the focus has mainly remained on the measurement of GHG emissions. There has been limited emphasis on the activity-level emission data analysis, and current methods lack a dynamic approach in the analysis of GHG emissions. These limitations contribute to the lack of GHG emissions evaluation and optimization strategies in prefabrication and modular construction projects. To address this knowledge gap, an earned green value management (EGVM) model based on the earned value management theory is developed for setting up a benchmark for evaluating the GHG emissions. Additionally, a two-phase GHG emissions optimization method based on the EGVM model is proposed. This EGVM model and the GHG emission optimization method were applied in a real prefabricated building project. The results demonstrate that this new approach can facilitate GHG emissions management and can improve decision-making in prefabrication and modular construction projects.

Large-scale public construction projects often become expensive white elephants requiring considerable postconstruction maintenance. Thus there is a pressing need to integrate construction and postconstruction stages into productivity measurement and analysis. However, previous studies of productivity measurement focused on the efficiency of the project delivery phase, but overlooked the utilization of the constructed facility. This paper developed a two-stage construction project productivity (CPP) measurement model by integrating site efficiency (SE) and utilization effectiveness to investigate strategies for improving the overall CPP. An as-usual setting, which delineates the measurement of CPP, was developed as the benchmark model. Given the relative nature of the productivity concept, two scenarios were considered for CPP improvement. The enhancement scenario was introduced by adding a requirement that CPP is enhanced compared with that in the benchmark model. Then a transcendence scenario was developed to ensure that CPP is better than SE. Strategies for CPP improvement were formulated and were analyzed using the analytical models developed. This study for the first time distinguished the substitutionarity and complementarity between facility occupation and facility management in the postconstruction stage, and claims that the opposite natures offer opposite strategies for CPP improvement. The empirical analysis revealed the law of diminishing marginal utility of facility management. This paper contributes to the body of knowledge by extending the productivity measurement model from the construction stage only to the integration of the construction and postconstruction stages, and by offering two productivity improvement scenarios for strategy formulation. Methodologically, this paper offers a two-stage productivity measurement model and its improvement models in a mathematical way. Managerially, based on the developed models, this work offers the evidence and quantitative guidance for the policy makers and public clients to strategically improve CPP of public projects.

The owner often entrusts multiple contractors to construct in parallel due to the linear distribution of a long-distance water diversion project (LDWDP). However, multiple contractors with more information are more likely to engage in opportunistic behaviors harming the project and the public interest. The tournament incentive mechanism is considered to be one of the most effective measures to solve this issue. There are few studies on tournament incentive in construction project management, and the heterogeneous characteristics of psychological preference and the differences in tournament form are ignored. Therefore, to bridge these gaps, the tournament theory is introduced in this study to design multicontractor incentive mechanisms considering fairness preferences and self-interest preferences in a LDWDP contract. The primary contributions of this study are (1) to develop multicontractor tournament incentive models considering the preference heterogeneity based on the Lazear-Sherwin (LR) tournament incentive model and the Fehr–Schmidt fairness preference model, thus remedying the lack of a multicontractor incentive mechanism in the construction project field; (2) to explore the impact of tournament grouping arrangement on the incentive structure and incentive effect under three scenarios to help the owners improve practical guidance for formulating an incentive mechanism; and (3) to design the incentive intensity associated with each contractor’s relative ranking and absolute output performance to provide a theoretical basis for the transaction governance of large projects involving multiple contractors. This study not only improves the optimal incentive contract for LDWDP theoretically but also enriches and promotes the development of behavioral contract theory in various fields.

Proper selection of subcontractors should consider multiple criteria such as cost, schedule, quality, and effective project management and control capabilities. Several multiple-criteria decision-making (MCDM) models that utilize a weighted score have been developed to help contractors select subcontractors. Although such models are capable of considering multiple evaluation criteria, they may unintentionally cover up some subcontractors’ deficiencies that can significantly impact project success. Considering such deficiencies is particularly important for high-stakes construction projects. The paper proposes a hybrid grey entropy relational analysis (GERA) model for subcontractor selection that integrates grey relational analysis (GRA) and the grey entropy model (GEM). The proposed GERA model evaluates subcontractors’ performance from two perspectives; proximity degree and equilibrium degree. The proximity degree is used to aggregate a subcontractor’s attributes into a comprehensive score, while the equilibrium degree is used to expose subcontractors’ deficiencies that should exclude them from selection. A case study is used to demonstrate the feasibility of the proposed model. The proposed model can be easily adapted to various decision-making scenarios with similar requirements.