Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic-based TBM Risk Management Model Using Bayes’ Theorem Considering TBM Accident Case Study
https://orcid.org/http://orcid.org/0000-0001-8889-1386
https://orcid.org/http://orcid.org/0000-0001-8205-5424
https://orcid.org/http://orcid.org/0000-0002-7986-8915
https://orcid.org/http://orcid.org/0000-0002-2040-8850
Risk management is crucial in ensuring the safety and efficiency of Tunnel Boring Machine (TBM) excavation by preventing adverse accidents. Unfavorable geology is a significant source contributing to such accidents, increasing the probability of their occurrence. However, conventional approaches have limitations in adequately addressing the complex and uncertain nature of accidents that occur during TBM tunnelling. This study proposes a probabilistic-based TBM risk management model based on Bayes’ theorem. The model utilizes case studies of TBM accidents to explore causal relationships within intricate and uncertain problems. The initial step involved establishing a TBM risk database through case studies of TBM accidents. Statistical analysis of the database enabled the computation of two types of probabilities: P(Source∣Accident) and P(Source∣AccidentC), for each causal combination. Subsequently, the posterior probability, P(Accident∣Source), was calculated using Bayes’ theorem. The risk level was determined based on the change ratio of the posterior probability compared to the prior probability, P(Accident∣Source)/P(Accident). The results of the study identified the fault zone and weak ground as the critical causes of face collapse. Furthermore, three out of five causal combinations related to excessive deformation were classified as having an intolerable risk level. Interestingly, none of the identified sources were associated with water/mud inflow accidents. In conclusion, the proposed model can serve as a valuable guideline for risk management in TBM construction, enhancing both safety and efficiency.
Probabilistic-based TBM Risk Management Model Using Bayes’ Theorem Considering TBM Accident Case Study
https://orcid.org/http://orcid.org/0000-0001-8889-1386
https://orcid.org/http://orcid.org/0000-0001-8205-5424
https://orcid.org/http://orcid.org/0000-0002-7986-8915
https://orcid.org/http://orcid.org/0000-0002-2040-8850
Risk management is crucial in ensuring the safety and efficiency of Tunnel Boring Machine (TBM) excavation by preventing adverse accidents. Unfavorable geology is a significant source contributing to such accidents, increasing the probability of their occurrence. However, conventional approaches have limitations in adequately addressing the complex and uncertain nature of accidents that occur during TBM tunnelling. This study proposes a probabilistic-based TBM risk management model based on Bayes’ theorem. The model utilizes case studies of TBM accidents to explore causal relationships within intricate and uncertain problems. The initial step involved establishing a TBM risk database through case studies of TBM accidents. Statistical analysis of the database enabled the computation of two types of probabilities: P(Source∣Accident) and P(Source∣AccidentC), for each causal combination. Subsequently, the posterior probability, P(Accident∣Source), was calculated using Bayes’ theorem. The risk level was determined based on the change ratio of the posterior probability compared to the prior probability, P(Accident∣Source)/P(Accident). The results of the study identified the fault zone and weak ground as the critical causes of face collapse. Furthermore, three out of five causal combinations related to excessive deformation were classified as having an intolerable risk level. Interestingly, none of the identified sources were associated with water/mud inflow accidents. In conclusion, the proposed model can serve as a valuable guideline for risk management in TBM construction, enhancing both safety and efficiency.
Probabilistic-based TBM Risk Management Model Using Bayes’ Theorem Considering TBM Accident Case Study
https://orcid.org/http://orcid.org/0000-0001-8889-1386
https://orcid.org/http://orcid.org/0000-0001-8205-5424
https://orcid.org/http://orcid.org/0000-0002-7986-8915
https://orcid.org/http://orcid.org/0000-0002-2040-8850
Risk management is crucial in ensuring the safety and efficiency of Tunnel Boring Machine (TBM) excavation by preventing adverse accidents. Unfavorable geology is a significant source contributing to such accidents, increasing the probability of their occurrence. However, conventional approaches have limitations in adequately addressing the complex and uncertain nature of accidents that occur during TBM tunnelling. This study proposes a probabilistic-based TBM risk management model based on Bayes’ theorem. The model utilizes case studies of TBM accidents to explore causal relationships within intricate and uncertain problems. The initial step involved establishing a TBM risk database through case studies of TBM accidents. Statistical analysis of the database enabled the computation of two types of probabilities: P(Source∣Accident) and P(Source∣AccidentC), for each causal combination. Subsequently, the posterior probability, P(Accident∣Source), was calculated using Bayes’ theorem. The risk level was determined based on the change ratio of the posterior probability compared to the prior probability, P(Accident∣Source)/P(Accident). The results of the study identified the fault zone and weak ground as the critical causes of face collapse. Furthermore, three out of five causal combinations related to excessive deformation were classified as having an intolerable risk level. Interestingly, none of the identified sources were associated with water/mud inflow accidents. In conclusion, the proposed model can serve as a valuable guideline for risk management in TBM construction, enhancing both safety and efficiency.
Probabilistic-based TBM Risk Management Model Using Bayes’ Theorem Considering TBM Accident Case Study
Lecture Notes in Civil Engineering
Wu, Wei (Herausgeber:in) / Leung, Chun Fai (Herausgeber:in) / Zhou, Yingxin (Herausgeber:in) / Li, Xiaozhao (Herausgeber:in) / Kwon, Kibeom (Autor:in) / Kang, Minkyu (Autor:in) / Hwang, Byeonghyun (Autor:in) / Choi, Hangseok (Autor:in)
Conference of the Associated research Centers for the Urban Underground Space ; 2023 ; Boulevard, Singapore
10.07.2024
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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