Journal of Construction Engineering and Management

Abstract

Lean manufacturing principles are being increasingly employed in off-site construction (OSC) with the primary objective of reducing waste and improving production efficiency. This is performed using several tools and technologies largely influenced by the concept of Industry 4.0 (I4.0) that sets fundamental design principles for technological development. However, the recent introduction of the concept of Industry 5.0 (I5.0) extends the I4.0 focus toward wider economic, social, and environmental implications. This study aims to evaluate extant literature employing lean tools and concepts in OSC toward the realization of I4.0 and I5.0 design principles, identifying key research themes and gaps and suggesting future directions. A mixed-method review was employed to first identify highly relevant literature using a bibliometric search. The identified references were then analyzed using qualitative content analysis through the lens of I4.0 and I5.0. Results highlight several interactions between identified lean-OSC tools and concepts and I4.0 and I5.0 design principles, signifying the power of these tools/concepts in meeting multiple industry objectives. The review also identifies a significant overlap between the resilience principle in I5.0 and many of the I4.0 principles, emphasizing resilience as an integrative concept of technological principles. Finally, several research gaps relating to the social and environmental aspects of lean-OSC research were identified, including research on mental health, assistive technologies, and design for end-of-life.

Practical Applications

Practitioners can benefit from understanding the capabilities of lean-OSC tools, such as process simulation and BIM, in addressing a wide range of design principles of I4.0 and I5.0. This paper also identifies the importance of exploring underexplored themes in the literature, particularly the mental health of workers in off-site construction environments. As the industry continues to evolve, construction professionals should consider incorporating new technologies and methodologies to monitor and support the mental wellbeing of their workforce. There is significant potential for improvement in adopting human-centered technologies, like collaborative robots and exoskeletons, which can empower workers and enhance workplace diversity and inclusion. This involves exploring innovative technologies that facilitate cross-lingual communication, gesture recognition, and intention prediction, providing opportunities for individuals with different languages, education levels, and disabilities to participate effectively. Furthermore, the research highlights the need to concentrate on environmental sustainability, specifically the integration of circular economy concepts into off-site construction practices. By examining the potential of lean principles in promoting end-of-life design considerations, such as design for disassembly and material passports, construction professionals can bridge the gap between OSC and circular economy concepts, creating a more sustainable construction industry.

This research devises a risk indexing method to assess the occupational health and safety (OHS) hazards associated with major sources of energy in the construction field, providing numerical inputs to project plan and schedule optimization. Further, the problem of “minimizing project schedule at lowest safety risks” (MPSLSR) is formalized to incorporate the concept of energy sources for OHS management in project planning and scheduling optimization. Instead of following commonly applied techniques to solve multiobjective optimization problems, the proposed research takes an alternative two-step approach to minimizing project duration and risk index, based on interpretation of path float in connection with the critical path method. This results in optimized project schedules that mitigate the substantial increment of OHS-related risks due to accelerating construction progress on projects through avoiding the incurrences of unnecessary activity time crashing and associated increases in OHS-related risks. The research application is demonstrated with (1) a tunnel construction project and (2) a made-up project featuring a large, complex network model.

Prefabrication is a promising construction method that helps to improve overall productivity, reduce the environmental impact, and improve on-site safety and security. To accelerate the use of prefabrication, we investigate the problem of stacking precast slabs, one of the most common prefabricated components, through mathematical programming methodology. A mixed-integer nonlinear programming model is proposed that incorporates many practical factors, including stack stability, reshuffling work, and dunnage positions. The model aims to minimize the reshuffling of slabs and reduce the area occupied by stacks, which are two crucial objectives considered during slab stacking and transportation. Through extensive experiments, the proposed method is proven to average a 16.56% improvement in optimality compared with the commonly used heuristic stacking method and is thus considered an effective tool for generating stacking plans.

Abstract

Construction workers are exposed to heat stress risks due to the combined effects of hot and humid weather conditions (HHWCs) and physically demanding work. A real-time activity intensity identification (AII) is required to measure the impact of HHWCs using a nonintrusive approach. This research developed a real-time AII system based on computer vision analysis (CVA). It then combined the CVA system with real-time video recordings to approximate workers’ activity intensity (AI) levels alongside HHWC records. A fundamental activities matrix was developed to build a list of measurable and identifiable features of site activities. These features were used to identify and link different postures to a crew’s AI and safety status within a given context. In real-site conditions, the AII system instantly and unobtrusively approximated workers’ AI and safety status under HHWCs. The system showed high detection performance with competitive deployment time, cost, and effort, outperforming previous related models. The results showed that formwork and steelwork are mostly moderate activities; however, moderate AI and HHWCs can create heat stress and fatigue and significantly affect workers’ safety, resulting in heat-related injuries and accidents. This research gives researchers and practitioners insight into the challenges associated with measurement methods and solving practical site measurement issues. This research promotes innovative methods for real-site measurements and contributes to knowledge in the field of safety and productivity in the construction industry by employing new, innovative CVA technology. This technology has applications in the industry by deploying a practical tool that could support aligned improvement in the safety and productivity of construction workers working under HHWCs.

Practical Applications

The challenges of automated and real-time measurements have always been of great interest to construction safety and productivity practitioners, particularly measurements of nonintrusive systems and competitive deployment time, cost, and effort. Such problems have also resulted in a substantial delay in making safety- and productivity-related decisions, which are major reasons for the increasing number of hot and humid weather–related injuries and incidents, particularly with the growing threat of global warming. Furthermore, under HHWCs, construction companies have also incurred significant productivity losses. This study offers an automated, nonintrusive, real-time measuring system with competitive deployment time, cost, and effort to monitor activity intensity and weather-related risks. Hence, site decision makers can make timely safety- and productivity-related decisions to improve work safety and productivity.

Large airport operational readiness (LAOR) is a critical factor that directly impacts the opening of airports to the public. However, limited research exists on the risks affecting LAOR schedules. This article explores the risk breakdown structure and risk interactions model of LAOR schedule risk. An empirical investigation was conducted on four large hub airports from 2009 to 2021 using grounded theory procedures. The study identified 21 risk factors categorized into management (highest frequency), technical, process, participant, and environmental risks (lowest frequency), which implied that risks primarily existed within a project rather than in the external environment. A systematic model incorporating risks and their interactions revealed that the primary risk transfer path was from the subject subsystem (participant risk) to the object subsystem (technical and process risk). The findings expand the knowledge domain of infrastructure risk and provide pragmatic risk evaluation and response guidelines.

Abstract

With the expansion of the scale of construction projects, the safety situation of construction projects has become more and more severe, and strengthening near-miss event management can effectively improve the safety management level of building construction sites and prevent safety accidents from occurring. However, the traditional monitoring and early warning systems rely on manual supervision, which is heavy and inefficient and cannot detect near-miss events in time. In the context of big data, blockchain technology, which is widely used, has the characteristics of decentralization, distrust, openness, nontamperability, and traceability, which can improve the safety of building construction sites to a certain extent. The primary objective of this study is to automatically monitor and early warn of near-miss events at building construction sites through the use of blockchain technology to effectively avoid the occurrence of building construction site safety accidents and reduce the incidence of safety accidents. To achieve this objective, we first constructed a knowledge base of near-miss events at building construction sites. Then, the near-miss event monitoring and early warning system for building construction sites based on blockchain technology was established. Finally, the near-miss incidents of not wearing safety belts working at height, which exists on the construction site, were used as an example to verify the feasibility and reliability of the monitoring and early warning system. The results show that the monitoring and early warning system has strong feasibility and practicability, and has certain research value. As the primary contribution, this paper developed a method of using blockchain technology for near-miss event monitoring and early warnings at building construction sites, which can realize real-time monitoring and automatic early warning, detect near-miss events in time, help trace the responsibility of events at a later stage, and improve the safety management level of building construction sites.

Practical Applications

This study suggests that blockchain technology can somewhat improve the safety management level of construction sites. This study developed a method for near-miss event monitoring and early warning at building construction sites by employing blockchain technology. The application of blockchain in the field of construction safety has attracted more and more attention, but the research on construction site safety is still in its infancy. With the continuous development and growth of the construction industry, construction site safety accidents occur frequently, but each accident is preceded by the occurrence of near-miss incidents. If near-miss events can be reduced and the management of near-miss events can be strengthened, the occurrence of safety accidents at building construction sites can be effectively reduced. In this paper, we introduce blockchain technology to secure data transmission and access using hash algorithms, realize timely warnings using smart contracts, verify and store data using blockchain data structures, and generate and update data using distributed nodes. By establishing a monitoring and early warning system for near-miss events at building construction sites based on blockchain technology, the occurrence of near-miss events can be effectively prevented and controlled, and the incidence of safety accidents can be reduced.

With a plethora of digital technologies, and some having high capital and running costs, it is critical to know which of these give architectural, engineering, and construction (AEC) firms a comparative advantage. Comparative advantage is measured using these metrics: higher project quality (Y1), productivity (Y2), ability to win tenders (Y3), and reputation (Y4), compared to AEC firms’ nearest competitors. This study investigated which specific digital technologies give rise to a comparative advantage for adopters in the AEC sector. The research design was an online survey using a structured questionnaire. Data were collected from AEC professionals in Singapore. The research found that the adoption of five digital technologies is significantly associated with higher project quality, productivity, and reputation compared to AEC firms’ nearest competitors. These ‘super technologies’ are cloud-based technology, design for manufacturing and assembly (DfMA), Internet-of-Things (IoT), robotic technology, and artificial intelligence (AI). The study discovered some digital technologies that are not correlated with any comparative advantage metrics. The contribution to knowledge is the creation of the digital technology adoption model that potential adopters may use to help them decide which specific digital technologies to adopt to achieve comparative advantage. For firms that are already adopting digital technologies, the adoption model serves as a benchmarking tool to measure comparative advantage. The implication for practice is for potential adopters to zoom in on the ‘super technologies’ and eschew those that do not provide comparative advantages.

Through an appropriate risk-sharing arrangement between governments and private partners, public–private partnerships (PPPs) are justified because of their improved value for money (VFM). From the agency theory perspective, an agency problem may arise when private partners pursue self-interests opportunistically at the government’s expense in the face of asymmetric information and conflicting interests. Transferring risk to private partners enables the government to encourage private partners to improve the performance of the project. The existing literature discussed the incentive mechanism of risk-sharing to resolve the external agency problem between the government and special purpose vehicles (SPVs) founded by private sponsors. However, the internal agency problem that occurs among private sponsors was not investigated. This study used an analytical framework based on the Nash bargaining game and principal–agent models to examine the effect of the internal agency problem on the optimality of risk-sharing between the government and SPVs. The findings indicate that the internal agency problem, which is characterized by the free-rider problem, can be mitigated through equity shareholdings negotiated by private sponsors. However, disregarding the internal agency problem leads the government to shift risk excessively or inadequately to the SPV. The results show that a government that acknowledges the internal agency problem determines the optimal risk-sharing with private partners, considering the dilution and squeezing effects brought about by privately negotiated shareholdings under different risk scenarios. This study contributes to the PPP knowledge body by introducing the internal agency problem into the incentive mechanism of risk-sharing between the government and SPVs. The findings also provide support for government to formulate risk-sharing strategies and shed light on the design of PPP incentive contracts.

The Hispanic construction workforce’s contribution to the construction industry is significant. However, this significant contribution is threatened due to higher injury rates among Hispanic workers. One contributing factor to the higher rates of injuries is that occupational safety and health (OSH) research studies have not been sensitive to their working conditions and unique challenges. Social equity in construction-related research helps identify and address the inequalities and barriers that certain groups of workers (e.g., Hispanic construction workers) may face in terms of accessing adequate training, resources, and support to ensure their safety on the job. This study aims to demonstrate how social inequity in OSH research impacts research findings and recommendations. Accordingly, this study replicates a recent study conducted on personal protective equipment (PPE) noncompliance. The findings suggested several statistically significant and practical differences in perceptions between Mexican and non-Hispanic workers. These differences may lead to an insufficient understanding of PPE noncompliance factors if unaccounted for. It is concluded that inclusive research studies are essential for producing reliable findings and practical recommendations. The results of this study can help scholars create better OSH research designs sensitive to workforce components. This will produce high-quality training materials and strategies to reduce the higher probability of injuries among workforce minorities, particularly Hispanic workers in the United States.

Construction sites remain one of the most hazardous workplaces globally. To improve workplace safety in the construction industry and reduce the personal injuries and socioeconomic impacts resulting from workplace accidents, tacit knowledge containing fundamental causes of accidents or specific contextual factors can be extracted from past accident narrative reports. However, manually analyzing unstructured or semistructured textual data stored in records is a daunting task, and requires the use of automated and intelligent technologies to achieve rapid and accurate knowledge acquisition. Therefore, this paper proposes a text self-classification model based on deep learning natural language processing (NLP) technology for automated classification of construction site accident cases by accident type. First, combined with two statistical measures, mutual information and information entropy, the preprocessed text data were subjected to phrase segmentation to identify more complete and accurate accident precursor information without human intervention. Then a complete multilayer and multisize convolutional neural network (CNN) model was constructed using pretrained Word2Vec word embeddings for text self-classification tasks. Finally, the test results of the CNN classification algorithm were compared with the practical application results of three shallow learning algorithms, and the performance of different types of classification algorithms was evaluated. The results showed that the CNN-based deep learning algorithm developed in this paper demonstrated excellent feature extraction and learning abilities in the task of automatic text classification in the field of NLP. This not only demonstrated that reliable accident prevention knowledge could be obtained from the textual descriptions of construction accidents, but also provided a novel model reference for document archiving and information retrieval.

Robot teleoperation, a control method allowing human operators to manipulate robotic systems remotely, has become increasingly popular in construction applications. A significant challenge is the disconnection between the robot sensor data and the human operator’s sensory processes, creating a sensorimotor mismatch in motor-intensive activities. This disconnection is particularly challenging in motor-intensive activities that require accurate perception and response. Researchers have started investigating haptic interactions to enhance the control feedback loop, including simulating contacts, motions, and tactile input. However, although current methodologies have advanced the field, they often focused on certain aspects and could be further expanded to provide a more comprehensive simulation of the physical interaction that occurs in typical construction operations. This study designs and tests a comprehensive high-fidelity embodied teleoperation method that simulates complete real-world physical processes via the physics engine. The proposed method captures all categories of physical interaction in typical motor-intensive construction tasks, including weight, texture, inertia, impact, balance, rotation, and spring. A human-subject experiment shows that the proposed method substantially improves performance and human functions in a teleoperated pipe-fitting task. The results indicate that the proposed multisensory augmentation method significantly enhances performance and user experience, offering valuable insights for designing innovative robot teleoperation systems for future construction applications.

Abstract

To address the construction industry’s fragmented resources and to facilitate its service-oriented transformation, a new service paradigm of a construction service trading platform (CSTP) enables the sharing and trading of distributed construction services. However, the current platform with centralized management cannot provide an online trading environment with information security and transaction transparency without the involvement of a trusted third party. Existing approaches to selecting services for complex tasks also ignore security and transparency, even though these are critical to the platform. This study selected services for complex tasks securely and transparently, and made three contributions. First, an innovative blockchain-based construction service trading platform (B-CSTP) is proposed, which can provide a secure and transparent trading environment. Second, smart contract–based task decomposition and service composition (TDSC) was established, in which three main functions were designed to implement the service selection approach for the complex task. Third, an Ethereum-based system was established to demonstrate the proposed B-CSTP and smart contracts for TDSC. A survey of construction professionals was established to evaluate the framework of the proposed B-CSTP, and experiments were conducted to evaluate the designed functions of the smart contract for TDSC. The results show that the proposed B-CSTP and the designed function of smart contracts are feasible. The survey reveals that 80% and 70% of survey participants had positive attitudes toward its security and transparency, respectively. Furthermore, the experiment results show that the design functions of smart contracts were effective and reasonable in selecting services. This study contributes to the new service paradigm and the problem of selecting services by combining blockchain and smart contract technology. This study expands the application scenarios for blockchain in CSTP and makes significant advances in using smart contracts for TDSC.

Practical Applications

Servitization has become one of the main directions for transformation in the construction industry. The new trading paradigm that adopts the electronic trading platform can facilitate value addition of construction services, serving as an application path to achieve servitization. This study contributes to this new trading paradigm by integrating blockchain and smart contract technologies, enabling service selection for complex tasks. The potential practical applications were illustrated from industry, enterprise, and individual perspectives. For the construction industry, a feasible implementation path for technology-enabled transformation to servitization, such as the proposed blockchain-based construction service trading platform, is presented. Enterprises that develop platforms can benefit from applying new technologies, such as introducing blockchain technology to solve security and third-party trust issues. These companies also can conduct new technology-enabled core businesses to enhance their competitiveness, such as the proposed select service function that combines blockchain-enabled smart contract technology. For individuals interested in frontier technologies, the application of blockchain and smart contract technology in this new paradigm provides a source of inspiration. The study has the potential to encourage the application of blockchain technology, facilitate the development of this new trading paradigm, and contribute to servitization transformation of the construction industry.

Due to the one-off nature of projects, which necessitates setting up of unique production sites, the construction industry requires stringent requirements for safety of the workforce and the public around job sites. Although there are various federal (e.g., OSHA) and local safety regulations (e.g., NYC Department of Buildings codes) to keep construction sites safe and incident free, around 20% of work fatalities in the private industry have been from the construction industry in the past years. Within the current practice, the requirements listed in these regulations are kept within document-based safety plans. The challenge is the large number of safety requirements to be checked with more than 1,000 sections and multiple narratives under each. Given this, the manual approach fails in tracking whether these requirements are properly considered in safety plans, hence resulting in omissions and accidents/incidents due to inadequate implementations of safety requirements. Although automated safety compliance-checking coupled with building information modeling (BIM) has been an opportunity to bring efficiency to the process, there are several challenges that stand in the way of the ideal practice. This research provides the results of rigorous research conducted with the guidance of practitioners in the construction safety and insurance domain, and analysis and implementation of OSHA Part 1926 narratives using model-based checking tools. The results have been consolidated and presented as a taxonomy of challenges along with potential solutions to these challenges. Researchers and practitioners (software vendors, construction companies, regulators) can build on the outcomes to generate solutions to address these challenges.

Abstract

During subway tunnel construction, the consumed time for each ring (unit of construction progress) is highly dependent on objective factors such as geological conditions and shield performances, as well as subjective reasons including workers’ proficiency and contractors’ management skills. It is nontrivial to score each ring’s efficiency from an objective angle, so that workers and contractors can receive fair evaluations. In this paper, a scoring method driven by construction big data is proposed. First, to resolve the high-dimensionality problem, a Gaussian mixture model (GMM) was employed to cluster rings of similar conditions in a probabilistic style so that detailed information can be retained. Second, the standard time to finish one ring was analyzed, so that each ring can be labeled as fail or pass, and our task can be considered as a classification problem. Third, for each cluster, a broad learning system (BLS) was developed as a classifier due to its advantages of fast computation and incremental learning. Finally, the BLS was trained with real tunneling data of 23,822 rings, and then scorecards were developed, where results of validation and statistical tests suggested that our method outperforms conventional ones. Feedback from the subway company and two compared contractors suggested that the proposed method is fair and practical, and it could reveal management problems that were easily overlooked.

Practical Applications

For subway shield tunnels, this study proposes a construction progress scoring method driven by big data for subway companies to evaluate the performance of each construction unit, e.g., each ring of the shield tunnel is treated as a sample analysis. First, in view of the difficulty of many geological types and risk variables, the Gaussian mixture method is used to classify the samples and obtain probability that each ring belongs to a certain geological type. Statistical analysis is used to calculate the shield standard time of each geological type, so that the time interval of pass can be determined. Second, a broad learning model with low calculation and updating cost is constructed to fit the mapping relationship between geological factors and construction progress. With this model, each ring can be scored. Finally, through the model training and verification of 23,822 ring data in seven subway intervals, results suggest that the method is practical. Our method can objectively reflect the work efficiency and management ability of different contractors and workers. In addition, suggestions can be elicited to improve the management level of construction units.

Construction organizations are project-based organizations in which both competencies and performance develop through project execution. A competency measure is a combination of knowledge, skill, processes, practices, and/or technology possessed by a construction organization. Performance measures are used to assess an organization’s competitiveness within its industry. An organization can analyze competencies to predict and improve performance. Previous studies investigated project- and organization-level competencies with respect to performance measures. However, multilevel assessment is needed, although it is challenging because the nature of construction competency and performance measures is complex, dynamic, and subjectively uncertain. To address these challenges, this paper presents a methodology for developing a fuzzy agent–based model (FABM) of competency and performance that models a set of organizational- and project-level competency measures as decision-making entities (i.e., agents) and predicts multiple performance measures as their emergent behavior. A case study was implemented to demonstrate, verify, and validate the proposed FABM model, with encouraging results in performance prediction. The academic contribution of this paper is providing a novel systematic, bottom-up modeling approach for measuring and assessing competencies at the project and organization levels and mapping these multilevel competencies to construction performance measures. Furthermore, the outcomes of this study are expected to support construction practitioners by providing a set of comprehensive hierarchical competency and performance metrics at the project and organization levels for managerial actions taken to identify, construct, and develop competency models to assess performance at both levels.

There are various shaped sections of slotted-in steel plates and timber grooves among the bolted timber joints with slotted-in steel plates to satisfy the design and assembly criteria in practical engineering. The different shapes affect the performance but have not been widely researched. To visualize the effect of shaped sections on the joints and provide some experience for targeting improving the ductility, we performed monotonic loading tests parallel to the grain on 16 sets of the proposed end U-shaped carbon fiber reinforced plastics (CFRP)–bolted timber joints with slotted-in corrugated steel plates. The effects of the wavelength, wave height, wave angle, timber thickness, number of CFRP layers, and interaction between the parameters were mainly considered. The results showed that the joints were susceptible to failure as a result of a number of manifestations, including shear damage, pressure damage, opening-mode cleavage in the grain direction (TL cleavage), CFRP cleavage, etc. Similarly, the joints had multiple combinations of yielding deformation modes of bolts, which included ${\mathrm{I}}_{\mathrm{S}}$, ${\mathrm{I}}_{\mathrm{S}}$ & ${\mathrm{III}}_{\mathrm{S}}$, ${\mathrm{III}}_{\mathrm{S}}$, ${\mathrm{III}}_{\mathrm{S}}$ & IV, and V. The load-displacement curve showed that the joints mainly undergo the elastic stage, the fracture failure transition stage, and the fracture failure stage without an obvious yield stage. According to the experimental results, an orthogonal parametric analysis of the mechanical properties was conducted and the degree and pattern of each parameter were obtained. Among them, each parameter affected the elastic stiffness relatively less; wave height and wave angle had relatively significant influences on the ultimate strength and ductility, and the ultimate strength fluctuated with the increasing wave height, but negatively correlated with wave angle, while the ductility was negatively correlated with both. Additionally, a strength formula was derived by theoretical analysis, and a good agreement was indicated by the average ratio of 1.03 between the theoretical and experimental results. This study contributes to the understanding of the effect of shaped slotted-in steel plates on the mechanical behavior of bolted timber joints with slotted-in steel plates and provides some theoretical and experience basis for the reinforcement. However, this study is mainly focused on spruce and slotted-in corrugated steel plates, which still need to be adequately investigated for other tree species and cross-sectional forms, especially the fracture characteristics. In addition, the group effect of bolted timber joints is more significant, which needs to be specifically studied for the design in engineering applications.

Information bottlenecks are a central issue in architecture, engineering, and construction (AEC) projects. It is crucial to detect their occurrence to mitigate their impacts. Existing approaches to detect information bottlenecks use metrics that require estimating the quantity of information published, which is a challenging task in practice. In this paper, building on the literature and recent developments in automation, we introduce a revised model that uses unresolved activities during project delivery and frequency of information publication actions to detect information bottlenecks. We validated our model by applying it to a case study. We compared bottlenecks detected by our model to ground truth obtained in the case study independently using observational data and external verification. We further study how project activities requiring different levels of interactions among project participants with different roles (e.g., designer, contractor) affect our model’s performance. Results support our model and show that it is possible to attain comparable performance by considering only high-interdependency activities. Our research enables teams and project managers to keep a focus on issues that require intense communication among different experts and organizations to avoid bottleneck. Our contribution to the body of knowledge includes a revised model that detects information bottlenecks during project delivery and insights into integrative project management where the focus is on both project activities and collaboration needs across roles to streamline the whole process.

Prescriptive approaches currently dominate decision-making in the case of general contractor–subcontractor contractual relationships. This leads to a general contractor–centric contractual practices and demonstrates a lack of exploration from the subcontractor’s perspective. Therefore, the purpose of this study is to develop a reliable and validated scale for analyzing the subcontractor–general contractor relationship in the construction industry. Through a review of literature and the application of quantitative methods such as reliability testing and confirmatory factor analysis, the study develops key concepts and measurement scales that allow for an analytical decision-making framework from the subcontractor’s perspective. The resulting scale can help practitioners make informed, analytical decisions in building relationships between subcontractors and general contractors, leading to a more holistic approach. This study is unique in its focus on developing a scale specifically for analyzing the subcontractor–general contractor relationship in the construction industry.

Abstract

Eliminating workers’ unsafe behavior is one of the most important goals for onsite management and behavior-based safety programs have been widely used. However, the existing method lacks consideration of the worker’s inner and personal factors causing the decrease in the effectiveness of the measures. This study puts forward a personalization method for safety interventions and aims to examine the effectiveness and practicability of this change in safety interventions to reduce construction workers’ unsafe behaviors. Personalization of safety interventions was achieved through a diagnostic intervention model targeting the construction worker’s cognitive-based competence, psychological needs, and safety motivation with consideration of personality traits. The workers’ data were collected via a questionnaire and smart helmets on a building construction site. The results indicate that addressing these inner factors could achieve a persistent positive effect on construction workers’ safe behaviors. The effect size of improvement on cognitive-based competence is more than that of safety motivation. Improvements in long-term memory retrieval ability, subjective norms, and risk tolerance are the most significant in the cognitive-based competence. Personalized safety interventions and the current safety management are compatible.

Practical Applications

This study identifies two drivers behind the construction worker’s behaviors and contextualizes them into cognitive-based competence and safety motivation. The experiment shows that considering individual internal causes through the diagnosis of the two behavioral drivers is important in enhancing construction workers’ safety behavior. These changes in safety management have been found effective: personality traits are useful in determining the emotional tone for the expression of safety communication; psychological needs help promote safety motivation; improving cognitive-based competence or/and safety motivation can be set as the objectives of safety interventions. The participation of managerial personnel, especially supervisor and site supervisors, can help improve both the safety communication and safety performance. Smart helmets are useful in the personalized safety interventions by recording the worker’s behaviors and delivering safety instructions. The findings guide project managers in designing specific behavioral interventions to reduce construction workers’ unsafe behavior and provide an experimental protocol for the future studies.

Abstract

Developing approaches for recognition and classification of awkward working postures is of great significance for proactive management of safety risks and work-related musculoskeletal disorders (WMSDs) in construction. Previous efforts have concentrated on wearable sensors or computer vision-based monitoring. However, certain limitations need to be further investigated. First, wearable sensor-based studies lack reliability due to vulnerability to environmental interferences. Second, conventional computer vision-based recognition demonstrates classification inaccuracy under adverse environmental conditions, such as insufficient illumination and occlusion. To address the above limitations, this study presents an innovative and automated approach for recognizing and classifying awkward working postures. This approach leverages multimodal data collected from various sensors and apparatuses, allowing for a comprehensive analysis of different modalities. A feature-level fusion strategy is employed to train deep learning-based networks, including a multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM). Among these networks, the LSTM model achieves optimal performance, with an impressive accuracy of 99.6% and an F1-score of 99.7%. A comparison of metrics between single-modality and multimodal-fused training methods demonstrates that the incorporation of multimodal fusion significantly enhances the classification performance. Furthermore, the study examines the performance of the LSTM network under adverse environmental conditions. The accuracy of the model remains consistently above 90% in such conditions, indicating that the model’s generalizability is enhanced through the multimodal fusion strategy. In conclusion, this study mainly contributes to the body of knowledge on proactive prevention for safety and health risks in the construction industry by offering an automated approach with excellent adaptability in adverse conditions. Moreover, this innovative attempt integrating diverse data through multimodal fusion may provide inspiration for future studies to achieve advancements.

Practical Applications

Mitigating potential risk factors for work-related musculoskeletal disorders (WMSD) in construction and improving safety and health performance are crucial in construction projects. Construction workers are frequently exposed to prolonged periods of awkward working postures. In pursuit of a more comprehensive solution, a pioneering and automated approach for the recognition and classification of such postures is developed. Specifically, this approach is rooted in the use of joint point data extracted from RGB images, synergistically fused with motion data representing the activity state and electroencephalogram (EEG) data representing the cognitive state. Rigorously tested, this approach demonstrates remarkable classification performance in deep-learning networks, boasting a maximum accuracy of 99.6%. Such high accuracy substantiates its potential for implementation in real construction management. Considering the inherent complexities of dynamic construction sites, compounded by challenging environmental conditions such as insufficient illumination and occlusion, automated identification methods commonly confront limitations in utility. In response, the integrated approach in this study amalgamates the rich information derived from diverse modalities, ensuring a sustained high accuracy rate of 94.9%. This not only demonstrates the exceptional performance of the new approach, but also its generalizability, thereby enabling proactive management of ergonomic and safety risks in construction sites.

Abstract

Subjectivity and uncertainty of risk assessment (RA) procedures can be improved by replacing guesswork with data-driven approaches such as machine learning (ML). Although a plethora of ML prediction techniques have been introduced to improve the reliability of RA procedures, the utilization of ML-based recommendation systems that can leverage data from multiple aspects has remained unexplored. In this study, a novel RA recommendation system (RARS) was developed to achieve more reliable, objective, and inclusive safety decisions that can prioritize hazard items and formulate related risky scenarios. To this end, a semisupervised graph representation learning framework, node2vec, was utilized to receive semantic and dependency information from safety records to recommend the components of potential accident scenarios (hazards, hazardous cases, dangerous activities, and risky behaviors) based on the given decision objective. The RARS’s ability to provide flexible and user-oriented safety recommendations was explored on a real-life construction accident data set. This allows construction safety practitioners to dynamically evaluate possible risky scenarios with details regarding different influential risk factors and accordingly devise more reliable site safety strategies and relevant policies.

Practical Applications

The proposed RARS, through its adoption of the graph representation learning-based recommendation model, has the potential to advance hazard identification and risky scenario formulation during the risk analysis and evaluation stages for three reasons: first, a relation-aware representation data set is structured while assigning each hazard item to the project, related safety features, and different construction occupations; second, it allows flexible configuration of the system input based on different decision objectives by the construction professionals; and third, it provides data-driven recommendations by learning the relationship between the characteristics of different safety data collected across various projects while considering the project similarities in terms of the shared safety attributes. The proposed RARS can identify patterns and relationships in construction safety data sets to generate suggestions and recommendations, even in the absence of explicit labels or outcomes. RARS can suggest relevant hazards, hazardous cases, dangerous activities, and risky behavior items, considering the safety features shared among different projects and construction occupations. This facilitates its constant utilization during the procedure of formulating different safety scenarios that are often performed based on experience-driven guess works, while there may be incomplete or missing data, which is a common occurrence in RA procedures.

Abstract

Rigid and flexible contracts, both of which have advantages and disadvantages, are widely used in public–private partnership (PPP) projects. How to choose an appropriate contract type to improve the performance of PPP projects is an important issue that governments need to address. The literature has focused mainly on the strategy evolution of governments and private investors under rigid contracts but has not considered the influence of contract type (rigid versus flexible) on their strategy evolution. In this study, by incorporating an other-regarding preference, evolutionary game models under rigid and flexible contracts in PPP projects are established. We compare the government’s supervision strategy and the private investor’s cooperation strategy under these two types of contracts and summarize the applicable situations. Our research results show that the type of contract (rigid or flexible) will not affect the government’s supervision strategy but will affect the cooperative strategy of the private investor. The applicable conditions of these two types of contracts are determined by profit sharing, the government’s expectation of the project performance, and the discount rate as well as other factors. There is no cross-influence between the government and private investor’s other-regarding preferences. The private investor’s other-regarding preference is conducive to reducing some opportunistic behaviors that cause damage to social welfare and in promoting cooperative behavior. This paper contributes to the literature on PPP project contract design by providing the applicable conditions for rigid and flexible contracts as well as contract choice suggestions for project managers in PPP projects.

Practical Applications

Choosing a proper contract type is important for the success of PPP projects. This study compares the strategy choices of government and private investors under rigid and flexible contracts, presents the applicability of those two contracts, and provides managerial insights and suggestions for the government to choose a suitable contract type and enhance project performance. First, for projects with small-scale or easy tasks for which private investors need to expend only a low level of effort, the government can pay less attention to the contract type. Second, for projects that have high prospects, i.e., will have high performance in the future (for example, infrastructure PPP projects in cities’ core areas), a flexible contract outperforms a rigid contract. Otherwise, a rigid contract outperforms a flexible one. Third, governments should pay more attention to the preliminary feasibility study of a project to avoid either over- or underconfidence and ensure a proper contract choice. Fourth, the government should focus on examining private investors’ personal abilities and behavioral preferences and choose private investors with high-capacity endowments and other-regarding preferences. Finally, increasing penalties for opportunistic behavior is not an efficient strategy for the government when a private investor has high cooperation motivation.

Abstract

Construction, by the nature of the work, is more accident-prone than other industries despite advancements in improving safety performance. Proactive mitigation and assessment of the safety performance of construction projects remain challenging due to the difficulty of acquiring, storing, and using data to produce accurate predictive models. This research focused on devising methods that allow decision makers to leverage existing data in the planning phase to streamline the development of predictive models. A data-driven approach to predict the probability of a safety incident occurring in a given construction project and within a novel discipline-level schedule is presented. By implementing the proposed model, decision makers can evaluate and mitigate the risk of a given project incident occurring by deploying discipline-level safety policies in the planning phase and modifying the schedule accordingly. A predictive model was developed based on selected safety-related metrics extracted from a data set comprising daily payroll data and incident reports, which represent 28 million working hours within eight different industrial construction projects in Canada. The model was implemented in a case study based on an industrial project to demonstrate the framework’s functionality and practical utility during the project planning phase. The results show that the revised safe plan can be achieved by incorporating safety considerations in the planning phase.

Practical Applications

This research provides a practical solution for enhancing safety in the planning phase of construction projects using a data-driven model. By leveraging existing historical data, decision makers can predict potential safety incidents within specific disciplines without the need for detailed quantitative planning information. This approach also enables effective adjustments to be made to the schedule in order to mitigate risks. Furthermore, the discipline-level approach facilitates proactive safety planning by implementing discipline-specific safety policies that align with the unique characteristics of each discipline. Using a case study based on an industrial project, the proposed framework demonstrated its functionality and practical utility by identifying suitable safety-related metrics that construction enterprises typically record. These sources can include safety-related data, such as incident reports, as well as data recorded for other purposes, such as payroll data. The results highlight that incorporating safety considerations in the planning phase enables the development of a revised safety plan. In conclusion, by considering safety-related metrics and utilizing human resources (HR) data available in all companies, organizations can proactively assess and improve safety performance.