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Risk identification is fundamental to effective risk management in any construction project. It is especially true for tunnel projects where technicality and complexity increase the risks, uncertainties, and challenges of realistic risk identification. However, two important issues need to be addressed in the current risk identification practice. First is the organization of previous project risk information (data) in unstructured and semistructured formats which hinders the use of emerging AI-based techniques and digitization of the risk management process. Second, overreliance on manual efforts and human experience, which is labor-intensive, error-prone, and time-consuming. To address these issues, this study proposes an intelligent question-answering system based on a knowledge graph and generative pretrained transformers (GPT) model. This study developed a tunnel risk knowledge graph (TRisKG) and then integrated the TRisKG with a GPT model to develop a question-answering system for tunnel project risks (QASTRisk). The proposed QASTRisk yielded a precision value of 97%, recall of 94%, and an F-1 score of 95%, indicating excellent performance compared to the existing studies. The proposed approach is appropriate for quick, effective, and intuitive risk identification at the early stage of tunnel projects as it can facilitate timely and reliable decision-making to prevent safety accidents and reduce project delays and cost overruns. Also, this work contributes to the body of knowledge by providing a framework for developing intelligent systems using artificial intelligence (AI) techniques to assist project managers in automatic and efficient risk identification at the preconstruction phase of the project. Thus, this enhances decision-making, improves project performance and the efficiency of project risk management practices, and increases productivity in the construction industry.
Isah et al. (Tue,) studied this question.