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TBM construction risk analysis based on fuzzy Bayesian network
Aiming at the characteristics of various TBM construction risk factors, fuzzy and complex systems, and frequent shutdown accidents, a TBM construction risk analysis methodology based on a fuzzy Bayesian network is proposed. This method mainly selects eighteen risk factors to construct an interpretive structural model. These factors affect the TBM construction from the four aspects of the geological risk, equipment risk, management risk, and technical risk encountered during the construction of a full-face mechanical excavation of the tunnel. A five-level hierarchical directed graph is extracted by matrix operation to determine the relationship between factors. In the case of incomplete data, the Bayesian network model can be established by employing the prior probability to describe the state of the root node through expert scoring, ideal for system predictions lacking data. Finally, the main risk factors affecting the TBM construction are introduced and discussed, and the TBM construction of the West Second Tunnel of the Beijing Water Supply Phase II Project is taken as an example to compare the evaluation results with those of the actual construction status. The obtained results are not much different from those of the actual situation, which proves that the proposed method is scientific and can provide a reliable basis for analogous projects.
TBM construction risk analysis based on fuzzy Bayesian network
Aiming at the characteristics of various TBM construction risk factors, fuzzy and complex systems, and frequent shutdown accidents, a TBM construction risk analysis methodology based on a fuzzy Bayesian network is proposed. This method mainly selects eighteen risk factors to construct an interpretive structural model. These factors affect the TBM construction from the four aspects of the geological risk, equipment risk, management risk, and technical risk encountered during the construction of a full-face mechanical excavation of the tunnel. A five-level hierarchical directed graph is extracted by matrix operation to determine the relationship between factors. In the case of incomplete data, the Bayesian network model can be established by employing the prior probability to describe the state of the root node through expert scoring, ideal for system predictions lacking data. Finally, the main risk factors affecting the TBM construction are introduced and discussed, and the TBM construction of the West Second Tunnel of the Beijing Water Supply Phase II Project is taken as an example to compare the evaluation results with those of the actual construction status. The obtained results are not much different from those of the actual situation, which proves that the proposed method is scientific and can provide a reliable basis for analogous projects.
TBM construction risk analysis based on fuzzy Bayesian network
Fangdi Xie (author) / Yong Zhou (author)
2024
Article (Journal)
Electronic Resource
Unknown
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