Development of a hierarchical fuzzy assessment model for quantifying GenAI risks in sustainable construction projects

Purpose The purpose of this study is to identify and evaluate the significance of emerging risks associated with the integration of Generative Artificial Intelligence (GenAI) into risk management (RM) within sustainable construction projects (SCPs). Methodology The research followed a four-stage methodology to collect and analyse data, including: (1) a systematic literature review to identify a comprehensive list of GenAI-related risks in the context of SCPs; (2) the development of a multi-criteria risk assessment model to determine key evaluation criteria; (3) the distribution of a structured survey to 80 construction experts to assess the identified risks based on these criteria; and (4) the development of a fuzzy-based model to quantify and rank the significance of the risks. Findings A total of 30 risk factors were identified and subsequently classified into five categories: input quality, technological adaptability, ethical and governance, information integrity, and financial-related risks. Furthermore, the fuzzy analysis revealed that the most significant risk factors were human error, data unavailability, insufficient training, data breaches, and lack of awareness. Implications The study provides both theoretical and practical contributions by developing a novel risk assessment framework tailored to GenAI integration in sustainable construction. The fuzzy set theory approach enhances the accuracy of decision-making in high-uncertainty environments and aids project managers and policymakers in prioritising critical risks. The findings also offer actionable insights for developing mitigation strategies and fostering responsible GenAI implementation. Originality/value This study makes a unique contribution by being among the first to systematically analyse the risks of GenAI integration in construction RM using fuzzy logic. It advances the understanding of AI-driven risks in the built environment and provides a replicable framework for future risk assessments. Moreover, it encourages further exploration of regional and contextual variations in GenAI risk perceptions and supports the broader digital transformation agenda in sustainable construction.