Abstract Construction quality risks critically affect both users’ experience and safety. Learning from the vast quality data generated by construction projects offers valuable opportunities for knowledge discovery, yet extracting reliable risk factors from unstructured texts remains challenging. Existing approaches often rely solely on term frequency and generic lexicons, overlooking causal relations and domain-specific nuances. In addition, the existing risk checklists insufficiently aligned with the liability clauses of inherent defects insurance (IDI). To address these limitations, this study develops a large language model (LLM)-assisted text-mining framework for extracting key factors from large-scale unstructured documents. The system integrates causal relation extraction powered by the LLM for obtaining explicit pairs, a domain-specific lexicon and synonym set derived from regulatory documents and expanded via LLM for improved segmentation and normalization, and an entropy-augmented term weighting scheme term frequency–information entropy (TF-H) to enhance the robustness of risk factor identification. Applied to 36,998 judicial judgments on construction quality disputes from 2000 to 2022 in China, the framework generates liability clause–aligned risk factor lists for IDI. The results demonstrate that the approach not only corroborates established risk factors but also reveals overlooked causes, highlighting the potential of LLM-driven text mining to enhance large-scale risk knowledge extraction. Furthermore, the findings provide practical implications for various stakeholders, including insurers, technical inspection institutions and project managers, benefiting them regarding the core workflows of IDI schemes-specifically inspection, underwriting, claims, and so on.
Liu et al. (Sun,) studied this question.