Acceptance and integration of Artificial intelligence and machine learning in the construction industry: Factors, current trends, and challenges

摘要

The construction industry, historically hesitant in adopting new technologies, is undergoing significant transformation with the integration of artificial intelligence (AI). This research delves into the various elements influencing AI acceptance and implementation within this sector. The study applies well-established models and theories of technology acceptance, including the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Innovation Diffusion Theory (IDT), specifically adapted to the unique context of the construction industry. Critical factors driving AI acceptance encompass perceived usefulness, ease of use, organizational readiness, top management support, and external pressures. Furthermore, the research highlights essential elements such as workforce skills, data availability, and cybersecurity concerns that considerably affect AI adoption. Current trends reveal an increasing utilization of AI in project management, predictive maintenance, and design optimization, with a notable surge in the adoption of AI-powered Building Information Modeling (BIM) and robotics. Despite these advancements, the construction industry encounters significant challenges, including high implementation costs, resistance to change, and a lack of standardization. This research offers a comprehensive review of the current state of AI in the construction industry, providing insights into evolving trends and ongoing challenges. Keywords: Construction Industry, Artificial Intelligence, Project Management, Decision Support Systems, Decision Making, Machine Learning, Construction Projects. Citation: Rane, N. L., Desai, P., & Rane, J. (2024). Acceptance and integration of Artificial intelligence and machine learning in the construction industry: Factors, current trends, and challenges. In Trustworthy Artificial Intelligence in Industry and Society (pp. 134-155). Deep Science Publishing. https://doi.org/10.70593/978-81-981367-4-9_4 4.1 Introduction The construction industry, one of the oldest and most essential sectors worldwide, has continually adapted to technological progress (Irani & Kamal, 2014; Oprach et al., 2019; Whitlock-Glave et al., 2019). Recently, artificial intelligence (AI) has emerged as a transformative element, poised to significantly enhance efficiency, safety, and overall project outcomes in construction (Mohammadpour et al., 2019; Patil, 2019; Akinosho et al., 2020). The adoption and integration of AI in this industry, however, are shaped by various factors and face notable challenges. The acceptance of AI in the construction industry is driven by several pivotal factors. A major incentive is the potential for substantial cost savings and efficiency improvements (Mohammadpour et al., 2019; Patil, 2019). AI technologies, including machine learning algorithms and predictive analytics, can optimize resource allocation, reduce waste, and streamline project management processes, resulting in considerable cost reductions. This is particularly appealing to construction firms operating in a highly competitive market with narrow profit margins. Enhancing safety on construction sites is another critical factor. AI-powered systems can monitor site conditions in real-time, predict potential hazards, and alert workers to dangerous situations (Darko et al., 2020; Sacks et al., 2020; Abioye et al., 2021). For instance, AI can analyze data from wearable devices to detect signs of worker fatigue or stress, thus preventing accidents. This proactive approach not only protects workers but also minimizes project delays and financial losses due to accidents. The increasing complexity of construction projects further drives AI adoption. Modern construction often involves intricate designs and sophisticated engineering requirements (Mohamed & Mohamad, 2021; Heo et al., 2021; Bolpagni & Bartoletti, 2021). AI can assist in managing these complexities through advanced modeling and simulation