Journal of Construction Engineering and Management

Managing design changes effectively can reduce cost overruns and schedule delays in construction projects. However, existing design change management in construction projects lacks integration with schedules, comprehensive design change information, and interoperability with other applications. This study aims to address the aforementioned limitations by presenting an integrated four-dimensional (4D) design change management model (DCMM). We developed this model by integrating design changes with 4D building information modeling (BIM) models, extracting informative data from model geometry and design files to provide essential information for design change impact analysis and management, interfacing with a data warehouse for efficient data management, and utilizing interoperable data schemas for cross-platform compatibility. The integrated 4D DCMM successfully validated its abilities through two case studies, despite certain limitations such as only focusing on tracking main structural building elements. Our study introduces significant contributions to the field of design change management in construction projects by (1) allowing design changes tracking along construction progress, thus enhancing project controls and facilitating effective communication among project teams, (2) providing comprehensive information for design change management and assessing structural integrity risks resulting from design changes, thereby improving inspection practices in construction projects, and (3) enabling universal interoperability of design change management files across various BIM applications, enhancing construction data asset management.

Cost estimating in the construction industry is challenging due to the high uncertainty associated with the supply chain, especially after the COVID-19 pandemic. Some research studies have addressed such problems by developing models that predict material cost. In fact, all material can be interconnected and interrelated with lead-lag relationships such that any inflation in one material’s price can be associated with inflation in other materials’ prices, referred to as transmission of inflation. Despite the latter, none of the existing studies have investigated inflation transmission among all construction materials. This paper fills this knowledge gap. The authors used a multistep research methodology. First, Producer Price Index (PPI) data for 16 construction materials were collected and sorted. Second, the vector autoregression technique was used to model the relationships between each pair of material and subsequently validate the associations using the Granger causality test. Third, network analysis was performed to identify the inflation transmission capacity (out-degree centrality), inflation susceptibility (in-degree centrality), and inflation intermediatory capacity (betweenness centrality) for each material. Finally, modularity-based clustering was conducted to categorize the materials based on their price indices’ interconnections and examine inflation transmission path among different sectors of construction-related material. The results show that metals and plastic products are generally the highest transmitters of inflation to other construction material including “Fabricated structural metal products” and “Plastic construction products.” Furthermore, the results show that “Concrete products,” “Flat glass,” “Brick and structural clay tile,” and “Architectural coatings” are also high transmitters of inflation and thus can be key indicators of increase in the overall construction cost. This paper provides the industry stakeholders with leading indicators and early warning signs for the inflated material prices. Contractors and owners can utilize those warning signs to enhance their procurement plans and budgeting decisions.

Abstract

The building sector has implemented code requirements to promote the adoption of all-electric heating systems and electric vehicle (EV)-ready in new construction, aligning with electrification goals. However, existing studies on adoption and equity indicators lack a comprehensive spatiotemporal perspective, impeding the evaluation of trends over time and across communities. To fill these gaps, this study investigates spatiotemporal inequalities and the link between EV charger and heat pump adoption, integrating community characteristics in Seattle using proposed inequality indices and multilevel vector autoregressive models. Key findings include a recent decrease in Gini inequalities for EV charger adoption and a consistent reduction in Gini inequalities for heat pump adoption over time. EV charger adoption demonstrates a stronger association with spatial inequality across tracts. Higher adoption variations over time for both technologies are observed in similar communities. Additionally, communities with higher EV charger adoption exhibit higher heat pump adoption, with only heat pump adoption Granger causing EV charger adoption within a community. Economic factors predict adoption patterns, linking higher income and income inequality to increased EV charger adoption but lower heat pump adoption. The findings suggest tailored strategies in building codes for promoting equitable clean energy technology implementation, considering technology-specific characteristics and spatial dimensions. Recognizing the causal links between technology adoptions is crucial for collaborating with grid operators, particularly in establishing building standards that prevent grid congestion. Aligning codes with clean energy intricacies promotes sustainable construction and urban environment.

Practical Applications

Striving for electrification in the building sector, the study explores clean energy technology adoption in Seattle. Positive trends emerge, with recent reductions in inequalities for EV chargers and consistent decreases for heat pumps over time. Notably, areas with higher EV charger adoption also experience increased heat pump adoption, with heat pump adoption leading to EV charger adoption. EV charger adoption demonstrates stronger spatial clustering, with higher adoption variations over time observed in similar communities for both technologies. Economic factors, like income, influence these patterns in the opposite direction to the technologies. To ensure a sustainable and fair transition, the research recommends tailored building code strategies, considering each technology’s unique aspects and community spatial layouts. Recognizing the interconnections between these technologies is crucial for collaboration with grid operators and implementing building standards that prevent congestion. Aligning codes with clean energy intricacies fosters sustainability in construction and urban development, contributing to a greener future.

Knowledge sharing (KS) and team creativity are crucial to achieving complex goals in a construction project. However, in the information age, there is a lack of research on exploring the influencing mechanisms among two dimensions of information technology (IT) innovations (i.e., intra- and interorganizational IT innovations) and two types of KS (i.e., tacit and explicit KS), along with team creativity in construction projects. Therefore, this paper builds a theoretical model of the relationships among the aforementioned variables. To validate the feasibility of this model, 289 valid questionnaires from the construction industry in China are analyzed. Results indicate that the influence of interorganizational IT innovation on team creativity is significant, and the influence is partially mediated by tacit and explicit KS. The influence of intraorganizational IT innovation on team creativity is significant, and the influence is partially mediated by explicit KS but not tacit KS. This study addresses the controversial issue of whether or how IT can promote tacit KS. This study also provides fresh insights into the influencing mechanisms of team creativity in construction projects owing to changes in the KS level affected by organizational IT innovations. There are also important practical implications relating to how project managers may rationally use IT to enhance KS and team creativity.

Training deep learning models for vision-based monitoring of construction sites usually requires a large amount of labeled data. Semisupervised learning methods can efficiently obtain unlabeled data with substantial cost savings. Thus, this paper proposes a semisupervised object detection method for construction site monitoring. Weather as well as strong and weak data augmentation are integrated to cope with the complex construction site conditions (weather changes, camera view shifts, and so on) by integrating semisupervised learning to leverage the valid information in unlabeled construction site images. To validate its effectiveness, the proposed method was tested on the Alberta Construction Image Data Set (ACID), a public data set for the construction research community. The experimental results revealed that the proposed method achieves an average accuracy [mean average precision (mAP)] of 81.1% when trained on only 3% of the labeled images. This study helps to significantly reduce the development cost of vision-based object detection models for construction sites.

Abstract

Developing real-time automatic intuitive devices based on electroencephalography (EEG) to improve hazard recognition performance (HRP) is essential in construction engineering management. However, existing studies generally developed devices based on unimodal data, failing to distinguish the role played by brain activities in different visual areas and ignoring the possible existence of other psychological mechanisms. Therefore, this study aims to use multimodal data based on brain-eye synergy to predict HRP and reveal the cognitive mechanism of hazard recognition to develop intuitive hazard recognition devices. Accordingly, we designed experiments to investigate this problem: (1) 53 construction workers completed a 120-trial hazard recognition task in a laboratory environment ($N=\mathrm{6,360}$), (2) an EEG recorder collected the participants’ EEG activities and an eye-tracking device recorded the data of eye movement, and (3) the power spectral density (PSD) of EEG was calculated and then imported into Matlab2018a in combination with EEGLAB for analysis. The results showed that the prediction results of hazard recognition have a time-variant effect. For fall-related hazards, EEG signals within 0–320 ms after the onset were significant in predicting HRP; for electric-related and fire-related hazards, EEG signals within 560–640 ms after the starting point were the most valuable. Also, compared with fall-related hazards, it took a significantly longer time to recognize electric-related and fire-related hazards, which consumed more attentional resources. Finally, the frontal lobe requires more attentional resources to finish cognitive activities than the occipital lobe to finish visual information processing activities, and the occipital lobe needs to process visual information first, and then transmit it to the frontal lobe for analysis and judgment. For the body of knowledge, this study, finding the time-variant effect of HRP, improves the theory of construction hazard recognition from an eye-brain synergy perspective and provides guidance for developing intuitive hazard recognition devices and improving management performance.

Practical Applications

This study combined eye-movement data and EEG data to predict the performance of hazard recognition using multimodal data. Not only did this study reveal the mechanism of hazard recognition, but it also proposed suggestions for project management and device development. Managers are suggested to implement refined management and regularly monitor the physiological signals of employees. Some construction companies started to use smart devices to measure workers’ arousal, emotional valence, emotional regulation ability, fatigue, workload, or safety attention level before work every day. And it is recommended to provide safety reminders to employees based on whether there are any abnormalities in their physiological indicators. In addition to project management, the results of this study can serve the development or optimization of hazard recognition equipment. Developing devices cannot only consider EEG information, but also combine EEG information with eye-movement information. Meanwhile, developers can also consider different types of hazards and different brain regions to improve device performance.

Construction remains a male-dominated industry, with women underrepresented both numerically and hierarchically. However, the growing shortage of construction professionals and skilled workers can be addressed by engaging more women in the industry. The present study sought to answer two research questions: “What are the major barriers hindering the participation of women in construction?” and “Is there any relationship between lean implementation and overcoming these barriers?” The study investigated women’s challenges and the relationship with Lean principles through a literature review and expert interviews. Two pillars of Lean, Continuous Improvement and Respect for People, were identified as potential mechanisms to support female practitioners, helping to eliminate the barriers identified. The study conducted 27 semistructured interviews to collect data on Lean benefits and their impact on gender diversity. Findings indicate that Lean provides team-related benefits, such as a psychologically safe and respectful environment, transparency, diversity, and inclusion, helping women build positive career experiences.

Abstract

Quality control (QC) is an essential component of quality assurance (QA) programs used to monitor and ensure the highway construction projects’ quality. After receiving the contractor’s QC plan, state departments of transportation (DOTs) conduct review and acceptance, monitor the QC process, and ensure construction quality by performing inspection activities. Given that state DOTs are increasingly implementing alternative contracting methods (ACMs), including construction manager/general contractor (CM/GC), design-build (D-B), and public–private partnerships (P3), it is essential to consider the impact of shifting more responsibility to the contractor on quality management activities, including developing and monitoring QC plans throughout the project development. The objective of this paper was to investigate the implementation of QC plans for administering QA specifications in highway infrastructure projects delivered using ACMs. The data collected from a national survey questionnaire of 43 DOTs in the US and structured interviews with seven DOT representatives were employed to investigate this research’s objective. As a result, eight typical requirements of QC plans in ACM projects were identified in terms of certification and qualification of QC personnel and process control. Additionally, the standard of care of QC staff in ACMs was not found to be higher than that in design-bid-build. Nine risk factors related to the oversight of QC plans in D-B, CM/GC, and P3 highway projects were identified. There was no statistical difference in the nine risk scores among D-B, CM/GC, and P3 projects. The findings from seven case studies provide insights regarding the implementation of QC plans for ACM highway projects. This study contributes to the body of knowledge by advancing the understanding of how state agencies execute and oversee QC activities in ACM projects. The outcomes of this study may help state DOTs identify optimal QC practices to administer the QA process in ACM projects.

Practical Applications

This study investigates the current state of practice in implementing QC plans to manage and improve QA processes for highway projects delivered using ACMs such as CM/GC, D-B, and P3. This study synthesized and documented typical QC requirements used by state DOTs in reviewing, accepting, and monitoring contractor compliance with their QC plans. Additionally, the descriptive and inferential testing results of nine risk factors in implementing QC plans in highway projects found in this study enable highway agencies to effectively justify their monitoring process of the QC plan implementation. Further, the results from seven case studies provide insights and key lessons learned regarding (1) the guidance and templates for developing a QC plan; and (2) risk perceptions in managing the QC plan implementation in ACM highway projects that can benefit both the contractors and state DOTs. The outcomes of this study also shed light on exploring the optimal allocation of responsibilities between state DOTs and contractors in implementing QC activities to administer QA programs in ACM highway projects.

This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model.

Evaluating the level of Building Information Modeling (BIM) implementation in construction firms is critical yet challenging in the absence of a quantitative method. This study addresses this gap. The study begins with a literature review that identified 27 BIM implementation enablers, followed by interviews with three firms to score their performance on each enabler. A mathematical model was developed to score a firm’s BIM implementation based on each enabler’s score. For each firm, 1 million random scenarios are generated to simulate alternative ways by which a firm’s enablers’ score can be improved. Subsequently, in each simulated scenario, the firm’s BIM implementation score is calculated. The simulation results are incorporated into a feature-pairing neural network that has been designed specifically to provide a customized best course of action for each firm’s further BIM adoption. The first contribution of this research is providing a comprehensive analysis of the dynamics and interconnectedness of factors influencing BIM adoption in AEC firms, offering insights into effective BIM adoption. The second contribution is proposing a novel quantitative approach for measuring the current level of BIM implementation and providing data-driven advice for steering the BIM implementation process. This research offers a practical contribution by providing companies with a tool to compute their BIM implementation score, allowing comparisons and benchmarking against competitors.

The production process of precast components is labor-intensive, involving various manual tasks. The physically demanding tasks usually result in fatigue and overexertion of workers, leading to increased occupational health risks and reduced productivity. An appropriate work–rest strategy is recognized to effectively promote both workers’ health and productivity, while it has rarely been studied in the field of the construction industry. To narrow this gap, this study developed a mixed-integer linear programming approach to optimize the work–rest schedule by integrating workers’ overexertion. The objective is to maximize the productive time affected by the workers’ accumulative fatigue and recovery. Also, the optimized work–rest strategy can be highly customized by considering personalized factors and task characteristics. Experimenting with a case study compared the default rest schedule provided by the superintendent onsite with the optimal solution solved from the developed model. Results suggested that up to 20% improvement in productive time can be achieved, especially for the task with a relatively higher workload. Computational experiments were conducted to evaluate the sensitivity of total productive time to various personalized and task-specific factors. The proposed method provides superintendents with an applicable strategy to improve workers’ productivity and reduce their occupational risks resulting from overexertion. This study can promote the implementation of personalized occupational health management and support the improvement of regulations on the required rest with quantified evidence, thereby contributing to more reliable scheduling and sustainable workforce development for the construction industry. The research scope was limited to the precast production process, and further investigation on broader applications will be conducted.

Abstract

The explicit safety knowledge contained in regulations in the form of texts and tables is crucial for construction safety management. However, the presence of rich semantic content within texts and the intricate layout of complex tables makes domain information extraction challenging. Therefore, this research proposed a hybrid approach to map safety knowledge graphs by automatically extracting information from both texts and tables in a scenario-oriented manner, combining rules and deep learning methods to achieve a balance between scene applicability and method flexibility. Furthermore, metrics from social network analysis (SNA) were applied to evaluate and verify the quality of the constructed knowledge graph. For extracting semantic information from text, the proposed approach supplemented the semantics information of the sentence and balanced the granularity of knowledge by combining the BERT-BiLSTM-CRF-based named entity recognition (NER) model and semantic role labeling (SRL)-based information extraction model. For irregular tables, a unified automatic extraction method was developed to process nested tables without preprocessing. The experiment constructed a comprehensive and scenario-oriented knowledge graph with 907 nodes, and showed high precision and recall for texts (89.37%, 85.42%) and tables (97.11%, 85.22%) on the test data. SNA results showed the proposed method ensured information richness and structural complexity.

Practical Applications

The construction safety knowledge graph constructed in this research offers three significant practical advantages. First, the proposed framework provides a solution for automatically integrating regulations into a knowledge graph with rich semantics and comprehensive information. Considering both sentence semantics and entity granularity enhances the application of Chinese regulatory clauses to specific construction scenarios. Second, the knowledge graph incorporated both textual semantics and tabular data, which assists managers in querying more accurate and comprehensive safety requirements. The comprehensive knowledge graph allows managers to quickly locate the necessary construction requirements on a larger scale and make more comprehensive and accurate construction decisions, effectively improving work efficiency and decision-making quality. Third, metrics from SNA suggested that the proposed method maintained the amount and diversity of regulatory information, while strengthening the compactness of the community structure and providing specific and clear requirements for the construction situation, operation procedures, and threshold definition. As a result, it is easier for managers to understand and process the safety information, perform construction operations in accordance with regulatory requirements, ensure the compliance of the operation, and further improve construction safety.

In this research, the multilayer perceptron (MLP), also known as error backpropagation neural networks, is made transparent and explainable by contrasting with the commonly applied multiple linear regression (MLR). A novel MLP-based method for performing global sensitivity analysis is formalized to tackle complicated, nonexplainable simulation models or artificial intelligence (AI) models, which were developed to support critical decisions in construction engineering. The sensitivity analysis results serve as further evidence to validate the decision support models and lend new insights into the problems under investigation. The proposed new method was applied in two case studies in construction engineering, they are: precast viaduct installation cycles and concrete strength development. In both applications, the results of sensitivity analysis were represented in straightforward forms and effectively cross-checked with the existing knowledge of the problem domain or the experiences of construction practitioners.

Unsafe behaviors may be aroused by inaccurate risk perception. Rather little is known about the measurement of risk perception ability in the construction industry. Based on the conception of risk perception ability, we proposed a measurement of risk perception ability, which conducted a questionnaire and a behavioral experiment. The specific measurement included an indicators system proposed to measure risk perception ability, which contained hazard identification degree, hazard identification accuracy, accuracy of the risk evaluation, deviation degree of the expected damage, deviation degree of the probability, and deviation degree of the risk evaluation. Based on the six indicators, the weighing values for each indicator were calculated, and a formula calculating risk perception ability (RPA) was proposed. Then, we applied the measurement to 327 construction professionals, which showed RPA differed from different construction professionals, and managers’ RPA was significantly superior to workers ($p=0.000***<0.05$). The method can measure construction professionals’ RPA conveniently and easily, which filled the lack of research regarding RPA in construction. Measurement results can help to provide targeted safety training recommendations, which is beneficial to reducing workers’ unsafe behavior and accident rates.

Adopting green technology innovation for buildings (GTIB) is an inherent and inevitable requirement for the building industry to realize green development. However, profit-driven construction developers often engage in opportunistic behavior and are reluctant to embrace GTIB, impeding the effective allocation of resources for innovative development in the market. Previous research has yet to extensively explore the decision-making behavior of construction industry stakeholders that drive GTIB. To explore the impact on critical stakeholders of developers adopting GTIB, this study develops an evolutionary game model involving developers, consumers, and the government, considering the role of developers’ opportunistic GTIB strategies and consumer pressure. Subsequently, this model elaborates on the stable operation path by enabling the analysis of the influence of critical factors and combined government regulation and the consumer pressure mechanism. The results show that improving the input–output ratios of green technology, reducing the incremental costs of green building, and setting a reasonable range of incentives are beneficial. Interestingly, reputation has only a limited influence. It is concluded that government action is needed to increase consumer participation. This study helps governments decide how opportunism can be best avoided in promoting the developers’ adoption of GTIB while providing theoretical support for designing an effective policy framework that combines government regulation and consumer pressure.

Abstract

Wind energy is widely deployed and will likely grow in service of reducing the world’s dependency on fossil fuels. The first generation of wind turbines are now coming to the end of their service lives, and there are limited options for the reuse or recycling of the composite materials they are made of. Current literature has verified that there is no existing recycling pathway (i.e., mechanical, chemical, thermal methods of recovery, etc.) for end-of-life materials in wind blades that can meet cost parity with landfilling in the US. However, to the authors’ knowledge there is no study to date that uncovers the cost structures associated with repurposing wind turbine blades in the US. Repurposing could offer a cost-competitive advantage through displacement of higher-value products, rather than materials or chemical constituents alone. This study implements life cycle assessment (LCA) and life cycle cost analysis (LCC) to assess the environmental and financial implications at each stage of repurposing wind turbine blades as the primary load-carrying elements for high-voltage transmission line structures in the United States. This case study contribution to knowledge is based on the successful management of construction waste by analyzing an application for repurposing construction demolition waste. Specifically, this study presents an environmental and financial analysis of repurposing wind turbine blades as transmission line poles. Under this case study, our results show that BladePoles have lower greenhouse gas emissions than steel poles, and we anticipate BladePoles will be less costly than steel poles. Overall emissions are most sensitive to combustion emissions, driven primarily by transportation distance and hours of required crane operations during the installation process. Compared to other evaluated recycling methods, repurposing wind blades as BladePoles has the least overall global warming potential.

Practical Applications

As renewable energy production grows, managing infrastructure at its end-of-life is increasingly relevant—for example, wind turbine blades. This case study presents a financial and environmental analysis of repurposing decommissioned wind turbine blades as transmission poles, called BladePoles. This paper presents the cost and associated greenhouse gas emissions at each stage of the process. The case study also compares this reuse application to typical steel pole deployment, finding that for the same 60-year life span and 161 kV, 230 kV, and 345 kV transmission line poles, the BladePole cost is lower than the steel pole. Greenhouse gas emissions are most sensitive to transportation distance from the wind farm to the transmission project and the time of crane use for installation are key parameters in this case study and reducing them directly reduces the total greenhouse emissions overall.