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Prefabricated construction (PC) plays a critical role in advancing the sustainable and high-quality transformation of the construction industry. Nevertheless, the fragmented and variable nature of technological innovations in PC complicates their acquisition, comprehension, and practical application, thereby hindering the process of innovation transformation. In response to these challenges, this study applies knowledge graph techniques to aggregate, correlate, and store knowledge pertaining to PC technological innovations. Specifically, using patent data from the past five years, and grounded in knowledge management and complex network theories, this study employs text mining, topic modeling, and association rule algorithms to perform clustering, evolutionary, and association analyses. The extracted entities and relationships obtained from the analyses are then stored in a Neo4j graph database for the construction and interactive visualization of a knowledge graph for PC technological innovation. According to the knowledge graph, a question-and-answer system framework is further proposed, providing practical application guidance. This research provides a comprehensive overview of the technological landscape, key nodes, and development trends in PC. It makes a meaningful contribution to knowledge management theory and complex network theory, advancing innovative applications in PC technology. [ABSTRACT FROM AUTHOR]

The scheduling of prefabricated component production is crucial for production efficiency and timely delivery in off-site construction (OSC). However, the prevalence of mixed-flow production in prefabrication renders conventional scheduling methods inadequate, leading to cost overruns and waiting issues. This study introduces a lean-based mixed-flow production scheduling approach to address the scheduling challenges in mixed-flow prefabrication. Guided by lean principles, this research analyzes the mixed-flow production process and process wastes, resolving issues of group production, pull production, and overall scheduling enhancement through multiobjective optimization modeling. A nondominated sorting genetic algorithm II (NSGA-II) based solution algorithm was established to minimize completion times, tardiness/earliness penalty, and type changes, thereby eliminating waste and ensuring continuous production. The effectiveness of the proposed approach was validated through a real case study, showing an average 1.84% reduction in makespan, 24.19% cost savings, and 36.03% reduction in waiting times compared to conventional nonlean optimization methods, while demonstrating good stability and reliability. This study innovates in engineering scheduling management and contributes to the development of lean construction theory, providing concrete support for component suppliers' successful delivery and promoting further development in OSC. [ABSTRACT FROM AUTHOR]

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application. [ABSTRACT FROM AUTHOR]

Prefabricated construction (PC) is regarded as an effective way to reduce carbon emissions in the construction sector and attracts increasing attention from academia and industry. However, the development trend of PC technology diffusion remains unclear, which is not conducive to improving the PC's promotion. To address this issue, this study predicts the diffusion trend of PC technology considering multi-factor interaction and reveals its underlying mechanism. The significance of influencing factors of PC technology diffusion is explored through the empirical research method based on partial least squares-structural equation modelling. The diffusion trend of PC technology is then predicted using the system dynamics considering the interaction of those key factors. The results indicate that both perceived superiority and knowledge sharing have positive influences on PC technology diffusion, and they mediate the path from organizational climate to PC technology diffusion. Network embeddedness influences PC technology diffusion through knowledge sharing. Under the above-factors interaction, PC technology diffuses continuously until stability in the next 48 months, with a diffusion speed trend of first increasing and then decreasing. The diffusion prediction of PC technology under multi-factor coupling in this study fills up the gap in existing research. The conclusions are suggestive for promoting PC technology effectively. [ABSTRACT FROM AUTHOR]