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<p>Risk management is essential for optimal decision-making during tunnel boring machine (TBM) excavation. Previous methods, however, have struggled with interpreting the uncertainties associated with TBM excavation and managing multiple concurrent adverse factors. This study proposes an interpretable risk management method that effectively addresses these uncertainties and concurrent sources. It employs an expert elicitation framework combined with fuzzy set theory to distribute the confidence levels of experts’ evaluations across multiple classes. The resulting impact, probability, and risk are presented as distributions, allowing for a comprehensive interpretation of expert judgment trends, which reveals how uncertainties are distributed and identifies any dominant class or risk level. The proposed method was applied to a slurry shield TBM tunnel project, where most distributions showed no single dominant class or risk level due to significant uncertainties, emphasizing the need for comprehensive interpretation. Furthermore, the method effectively addressed increased probabilities and risks stemming from concurrent factors, in contrast to when these factors were considered individually. Comparative analysis demonstrated that even meaningful but minor responses can significantly influence the determination of risk levels through comprehensive interpretation.</p>

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An interpretable framework for risk management in TBM excavation using expert elicitation integrated with fuzzy set theory

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Kibeom Kwon,
  • Minkyu Kang,
  • Young Jin Shin,
  • Byoungcheol Ahn,
  • Hangseok Choi

摘要

Risk management is essential for optimal decision-making during tunnel boring machine (TBM) excavation. Previous methods, however, have struggled with interpreting the uncertainties associated with TBM excavation and managing multiple concurrent adverse factors. This study proposes an interpretable risk management method that effectively addresses these uncertainties and concurrent sources. It employs an expert elicitation framework combined with fuzzy set theory to distribute the confidence levels of experts’ evaluations across multiple classes. The resulting impact, probability, and risk are presented as distributions, allowing for a comprehensive interpretation of expert judgment trends, which reveals how uncertainties are distributed and identifies any dominant class or risk level. The proposed method was applied to a slurry shield TBM tunnel project, where most distributions showed no single dominant class or risk level due to significant uncertainties, emphasizing the need for comprehensive interpretation. Furthermore, the method effectively addressed increased probabilities and risks stemming from concurrent factors, in contrast to when these factors were considered individually. Comparative analysis demonstrated that even meaningful but minor responses can significantly influence the determination of risk levels through comprehensive interpretation.