Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.
The construction industry has a high accident rate, making safety training for construction workers increasingly important in addressing this issue. Previous research on construction safety training has made significant progress, but existing literature reviews lack a comprehensive summary of the development of construction safety training, an evaluation of the effectiveness of current training methods, and an analysis of potential improvements in the future. Therefore, this study addresses these challenges through systematic literature review, scientometric analysis, and meta-analysis. A total of 92 publications since 2001 were included in this study after searching and selection based on the preferred reporting items commonly used in psychological and medical systematic reviews and meta-analyses. This study identified three phases of construction safety training research: determination of the necessity and significance of construction safety training, exploration of different methods for construction safety training, and development of construction safety training tools using advanced technologies. Findings from the scientometric analysis revealed that prevention and hazard recognition are key research topics in the field. Critical findings of prevention and hazard recognition were summarized and discussed to have a comprehensive understanding of current progress. The results of the meta-analysis indicated that current safety training methods are generally effective in improving construction safety, though the effectiveness in field studies is lower than in laboratory studies. Thus, researchers should understand why safety training performance is different between laboratories and construction sites and then control the differences to enhance the effectiveness of construction safety training in the future. This study identified that personalized safety training is a foreseeable development trend and suggested more efforts on the improvement of long-term safety training effectiveness. Additionally, safety training methods should be revised and optimized for the needs of intelligent construction and prefabricated construction. These findings offer new insights into construction safety training for researchers and policymakers.
With the rapid development of building information modeling (BIM) and Industry Foundation Classes, BIM-based automated code compliance checking (ACCC) has long been desired by the architecture, engineering, and construction (AEC) industry. Numerous studies on the technological issues of BIM-based ACCC have been conducted over the past decade, but no studies have focused on the acceptance of BIM-based ACCC by the AEC industry. Understanding how the industry views BIM-based ACCC and the factors that influence the industry’s perception will give a clear direction to research, development, and application of this process in both technological and managerial areas. To fill the gap, this study used a hybrid approach that incorporates Decision-Making Trial and Evaluation Laboratory and Interpretative Structural Modeling methods to study the factors influencing the acceptance of BIM-based ACCC in the AEC industry in China in terms of five different dimensions (technology, organization, individual, environment, and economy) to reveal the importance of each factor and the interaction among factors. Data in this study were collected from both the design sector and the construction sector. Similarities and differences in attitudes toward BIM-based ACCC in firms in the two sectors are fully addressed and compared, and suggestions to encourage better acceptance and adoption of BIM-based ACCC in each of these sectors are provided.
Current national occupational safety and health (OSH) initiatives follow reactive approaches, i.e., if it breaks, fix it. Existing accounts, however, failed to improve national OSH performances substantially, which imposes the need for an in-depth and proactive (fix it so it will not break) investigation of national occupational fatality risks. Despite many studies examining the fatality risk of workers based on project-, company-, and/or behavior-related factors, the role of national conditions on the countrywide fatality risk of workers has not been explored. The present research leverages the national statistics of Turkey to examine their influence on construction workers’ fatality risk through a machine learning–based prediction model. Several widely used machine learning methods were adopted for determining whether the upcoming month poses a significant fatality risk for construction workers or not based on national statistics of the previous month. According to analysis results, the gradient boosting decision tree algorithm yielded the highest prediction performance in terms of f1-score. The recently developed game theory–based Shapley Additive Explanations (SHAP) algorithm was used to identify whether and how national conditions affect countrywide fatality risk of construction workers. Findings illustrate that the share of the construction sector in employment, market demand, and labor shortage are the most significant national factors in determining the fatality risk. SHAP summary and SHAP dependence plots are further presented to provide decision makers with a clearer understanding of hidden relationships between fatality risk and national conditions. In addition, a framework that can be practically used by policy makers and governmental authorities is developed to help minimize national occupational fatality risk. Overall, predicting national fatality risk in the industry and identifying the national precursors of occupational fatalities contribute to the development of macrolevel safety improvements based on country-specific conditions.
During the rapid development of building information modeling (BIM) in the construction industry, a phenomenon of nonidentification with or even resistance to BIM has emerged among technical staff in construction projects. Based on Information technology (IT) identity theory, this study proposes a concept of BIM identity to reflect technical staff’s identification with BIM technology. As an important driver of BIM adoption, institutional pressures have received little attention in the literature at the individual level in terms of their effects on the psychological states of BIM practitioners. Therefore, this study proposes a theoretical framework to investigate the impacts of institutional pressures on BIM identity and to explore the mediating role played by perceived usefulness in this context. Based on 284 questionnaires collected from BIM practitioners, the results show that (1) coercive pressure, mimetic pressure, and normative pressure are all positively related to BIM identity; (2) perceived usefulness fully mediates the positive relationship between normative pressure and BIM identity, and partially mediates the positive relationships between the other two forms of pressure and BIM identity; and (3) the impact of mimetic pressure on perceived usefulness or on BIM identity is significantly stronger than that of coercive pressure or normative pressure. The findings contribute to the extant BIM research by revealing the influencing mechanism of external institutional pressures on BIM identity of individuals, which helps to understand the psychological process that leads to BIM adoption behaviors. In addition, this study provides guidance for project managers to take measures to create coercive, mimetic, and normative pressures to cultivate BIM identity of BIM practitioners, improve BIM performance of construction projects, and ultimately improve productivity of the construction industry.
The spread of airborne infectious diseases has largely been driven by superspreading events, in which a single individual directly infects several contacts. Superspreading events that occurred at several construction sites around the world afflicted construction practitioners and forced the suspension of construction activities. To reduce the probability of superspreading events, this study developed a network-based computational framework based on a K-shell decomposition approach with the input of the topological interaction network of project participants to identify potential superspreaders in construction projects. The feasibility of the developed framework was evaluated with three numerical case studies: one sample case with a hierarchical structure with an average accuracy of 98.45%, one sample case with a matrix structure with an average accuracy of 92.25%, and an empirical case related to a COVID-19 outbreak in a construction project in Hong Kong with an accuracy of over 80.13%. This study recommends that all potential superspreaders, especially if they are employed by the main contractor, take rapid antigen tests (RATs) regularly. If all potential superspreaders are detected through regular RATs and all potential secondary cases are detected by contract tracing, up to 82.35% of infected cases can be prevented.
Construction bid documents may contain uncertain or incomplete information that can affect project pricing as well as project performance, if not addressed prior to bidding. To resolve the uncertainties and clarify project requirements, the risk and uncertainties prevailing in the document should be identified at an early stage of the project life cycle. In this study, pre-bid request for information (RFI) is utilized as a key clue to quantify project ambiguities and uncertainties of a bid document, as pre-bid RFI is generated by bidders when any ambiguous or incomplete information is encountered in the bid document. Despite the significance of pre-bid RFI in quantifying project uncertainty, studies considering pre-bid RFI to identify project uncertainty are limited. Driven by document-based analysis, this study aims to investigate what uncertainties are frequently encountered in bid documents and how they affect project pricing. To achieve the research goal, this study will (1) identify the prevailing risks/uncertainties in the bid document; (2) determine the most common risks/uncertainties and their impacts on bid price; and (3) verify the significance of pre-bid RFIs in bid uncertainty prediction models. To achieve these objectives, public project data from US state Departments of Transportation (DOTs) were collected and used for frequency analysis, correlation testing, and machine learning-based prediction models. The results of uncertainty prediction models showed that uncertainties driven by pre-bid RFI analysis can improve the project risk prediction up to 15%, verifying the significance of RFIs in the bid price prediction model. This study will contribute to the construction management body of knowledge by clarifying the likelihood of errors and uncertainties that should be checked before bidding, thereby proactively preventing future design changes, claims, and dispute risks.
Current airport infrastructure is in a state of decline, with reports scoring it at an underperforming classification of D+. To address this issue, significant improvement and advancement of the infrastructure is needed. With backing on an authoritative level, the nation can expect an increase in the number of improvement projects. Airport stakeholders have long been accustomed to delivering their projects using traditional methods, such as design–bid–build (DBB). Design–build (DB) is an alternative delivery method that has added benefits for project metrics, such as schedule and cost performance. There is a lack of research evaluating DB within the context of airport projects. This study fills this knowledge gap. The goal of this research is to provide an improved understanding of DB with respect to DBB on fundamental key risks that impact schedule and cost performance in airport projects. This goal is achieved by a multistep interdependent methodology comprised of: (1) collecting and assessing data on 34 risk factors, (2) calculating the risk ratings of each factor, and (3) statistically analyzing the risks for their actual effect, as well as how they are perceived by between different stakeholder groups. The results show that the traditional DBB delivery method results in greater risks for most risk factors than does DB. Furthermore, contractors perceived DBB more negatively than DB. The top significant risk in DBB is the low level of team collaboration. Conversely, while statistically insignificant, unclarity or incompleteness in project scope was the most critical risk factor in affecting DB. To this end, DB implementation has promise for handling many risks better than DBB, and greater integration of DB should be prioritized in future airport projects to reap those added benefits. Ultimately, this research contributes to the body of knowledge by providing insight for airport stakeholders on the crucial risk factors that must be considered in project delivery.
Establishing a fair benefit distribution system for construction projects, in which participants often need to work together in a highly uncertain and interrelated environment, is challenging. There is a lack of objective mechanism for construction projects to motivate reliable workflow automatically and instantly. The objective of this study is to develop Shapley value–based smart contracts to automatically assign fair rewards/penalties to motivate task-level collaborations. The research first developed a simulation model to quantify subcontractors’ marginal contributions under different coalitional scenarios. Then, the simulation results were aggregated using Shapley value to determine each participant’s reasonable rewards/penalties. Lastly, the payment was encoded in the smart contract and then deployed in the blockchain to self-enforce consensus executions. The results showed that Shapley value–based smart contracts exhibited incentives to motivate reliable contributions and enable peer negotiations to realize task-level production. The contributions of this study to the body of knowledge are (1) quantify subcontractors’ marginal contributions to the project, and (2) determine how to distribute fair collaborative outcomes when project participants can perform at different levels of effort. The incentives embedded in smart contracts can reshape project participants’ collaborative behaviors toward desired outcomes, enabling a self-manage, self-govern, and self-adjust decentralized autonomous organization.
Effective implementation of modularization demands close collaboration among the various project stakeholders due to the distinct and complex needs of such construction method. In fact, lack of adequate collaboration is one of the main factors impacting modular construction performance. Despite that, no previous study has yet addressed collaboration requirements in modular construction and their cascading failure impact on project performance. This paper fills such a knowledge gap. To this end, the authors followed a multistep research methodology. First, systematic literature analysis was performed to identify the factors impacting collaboration and the impacted modular risks as well as their cause–effect relationships. Second, two surveys were distributed to collect (1) importance weights and failure probabilities for the collaboration factors; and (2) failure probabilities and performance impacts for the modular risks. Third, network analysis was conducted using in- and out-degree centralities to determine the most influential and sensitive aspects in terms of collaboration. Fourth, independent cascade modeling was performed to capture the cascading failure effect of various collaboration aspects on project performance. Ultimately, a total of 25 factors were found to impact collaboration categorized under four themes, including (1) project organization and control, (2) stakeholders’ relationships and characteristics, (3) information sharing, documentation, and technologies, and (4) design and construction planning. Furthermore, 10 modular operation risks were found to be impacted by collaboration in construction projects. Although the most influential factors were related to information sharing, documentation, and technologies, the most sensitive factors fell within the design and construction planning. Most importantly, results show that inadequate collaboration during design and construction planning can lead to 70.6% direct growth in schedule and cost of modularized projects. This paper contributes to the body of knowledge by offering an unprecedented framework that investigates collaboration requirements in modular construction and their interdependencies.
The primary objective of this research is to demonstrate the feasibility of a model-based construction safety assessment system using building information modeling (BIM) and diagnosing accident-prone BIM objects through prevention through design (PtD). Although extensive research has focused on early risk detection and accident predictions in safety, previous approaches have often missed opportunities to identify safety issues arising from design choices. Potential safety risks have been assessed retrospectively by reconstructing safety concerns based on completed design options. To address this gap, this research aims to provide foresight regarding construction safety hazards from the early design stage. First, risks embedded in design decisions are identified by analyzing safety incident reports using text-mining techniques. Then, the relationships among design elements, accident precursors, and risk events are established through if-then relationships. The potential hazards associated with design choices are evaluated by developing and running visual scripts and assessing design model parameters in BIM. This approach enables architects to track construction risks during their design stage, even without extensive onsite construction experience. In addition, owners can evaluate design decisions considering construction safety risks, and contractors can forecast and monitor risky elements, materials, and locations during construction execution. The research outcomes contribute to enhancing safety risk awareness in the early design phases and support efficient and predictive safety management during construction.
Prefinished prefabricated volumetric construction (PPVC) represents an innovative approach within the Design for Manufacturing and Assembly (DfMA) framework aimed at enhancing efficiency in the built environment sector. Despite its promising potential, research on PPVC adoption and its associated benefits remains scarce. This study evaluated the existing level of PPVC adoption in Singapore’s built environment industry; investigated perceived benefits at industry, organizational, and project levels; and examined project performance indicators and critical risk factors related to PPVC. A comprehensive literature review yielded 28 performance risk factors, which informed a survey questionnaire targeting major disciplines within the industry. Analysis of survey data, coupled with postsurvey interviews, revealed that 57.45% of respondents had not yet participated in PPVC projects, and 55.32% of companies anticipated involvement in at least one PPVC project within the next 3 years. Respondents acknowledged the advantages of PPVC and its positive impact on cost, schedule, and quality performance. The top three risks associated with PPVC projects were found to be Volatile Economic and Social Conditions (C5), Poor Jointing (B3), and Delay in Module Delivery and Transportation (A1). These findings contribute to the scholarly discourse on strategies to increase PPVC adoption and inform policy-making, promoting a more efficient built environment sector.
In the construction industry, reducing the quantities of engineered materials provides a significant opportunity of mitigating the environmental impacts caused by material production and processing. Although the efficient use of materials has been receiving considerable attention in the building industry, there has been little research aimed at measuring the material use efficiency (MUE) of a project. The goal of this study is to fulfill this gap by using data envelopment analysis (DEA) as a benchmarking technique to measure the overall MUE performance of a building project and to further compare the performance against peer projects in order to promote enhanced efficiency through target setting. In this study, the efficiency is measured by adopting the quantities of a variety of materials consumed during construction as inputs and the floor area of a built facility as an output. To generate a more reliable efficiency score, a stepwise variable selection scheme is first applied and then the MUE scores of projects are ranked based on their cross-efficiency. In addition, clustering analysis and DEA are fused to enable a more realistic target to be set for each input, thereby determining practical, yet challenging targets for each underperforming project. A sample of 12 healthcare projects is used as a case study to demonstrate how the proposed MUE benchmarking model can be used to measure and improve the MUE. The results reveal that the model enables projects to evaluate their MUE performance, recognize the gap with the best-performing projects, and help them determine the targets to become efficient.
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