Can a chatbot enhance hazard awareness in the construction industry?

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review

The construction industry faces many challenges, including schedule and cost overruns, productivity constraints, and workforce shortages. Compared to other sectors, it lags in digitalization in every project phase. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies revolutionizing the construction sector. However, a discernible gap persists in systematically categorizing the applications of these technologies throughout the various phases of the construction project life cycle. In response to this gap, this research aims to present a thorough assessment of the deployment of AI and ML across diverse phases in construction projects, with the ultimate goal of furnishing valuable insights for the effective integration of these intelligent systems within the construction sector. A thorough literature review was performed to identify AI and ML applications in the building sector. After scrutinizing the literature, the applications of AI and ML were presented based on a construction project life cycle. A critical review of existing literature on AI and ML applications in the building industry showed that AI and ML applications are more frequent in the planning and construction stages. Moreover, the opportunities for AI and ML applications in other stages were discussed based on the life cycle categorization and presented in this study. The practical contribution of the study lies in providing valuable insights for the effective integration of intelligent systems within the construction sector. Academically, the research contributes by conducting a thorough literature review, categorizing AI and ML applications based on the construction project life cycle, and identifying opportunities for their deployment in different stages.

Willingness to accept metaverse safety training for construction workers based on extended UTAUT

Safety training (ST) is essential in avoiding unsafe behavior of construction workers. With the rise of metaverse technology, metaverse safety training (MST) has gradually become a new model to guide construction workers in safety production. An in-depth study of construction workers' willingness to accept the metaverse safety training (WAMST) helps improve its effectiveness, but studies need to pay more attention to it. This study constructs a conceptual model of WAMST for construction workers, and the influencing factors of WAMST are explained based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT). It established a Structural equation modeling to verify the relationship between influencing factors. An example verifies the feasibility of the model. The results show that the framework significantly contributes to the willingness of construction workers to participate and improves safety awareness. Specifically, performance expectancy, effort expectancy, social influence, and convenient conditions significantly affect the construction workers' willingness to accept. Convenient conditions have a direct effect on actual behavior. Willingness to accept plays a mediating role between performance expectancy and actual behavior. Perceived trust moderates the effect between willingness to accept and actual behavior, and the force of positive interpretation increases proportionally. It confirms how to improve the safety capacity of construction workers and provides references for governments, enterprises, and projects to formulate ST strategies.

A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management

Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects' cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption.