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Risk assessment of TBM jamming based on Bayesian networks
https://orcid.org/0000-0002-6578-7583
Abstract Tunnel boring machine (TBM) jamming is one of the main causes that declines the efficiency of excavation rate, and results in construction delay and huge economic loss. A method combining the interpretative structural model (ISM) method and the Bayesian network (BN) is proposed in this study to assess the risk of TBM jamming. The geological disasters directly causing TBM jamming and other key influencing factors are concluded by analyzing 120 engineering cases (detailed information can be found in Appendix). A multi-layer directed node graph indicating the interrelations between the factors is built by the ISM method. And the node graph is used as the structure of BN. Expectation maximization (EM) algorithm is used for parameter learning to obtain the final BN model. Posterior probability analysis shows that tunnel collapse and large deformation of surrounding rock are the main disaster types causing TBM jamming; the rock mass classes, fault zone, and underground water are the main influencing factors. The BN model has been successfully applied to a tunnel in China. It can dynamically predict the probability of geological disasters and TBM jamming according to the geological conditions of tunnel face and the detection results of advance geological prediction. The results of this study can provide some references for the prevention of TBM jamming.
Risk assessment of TBM jamming based on Bayesian networks
https://orcid.org/0000-0002-6578-7583
Abstract Tunnel boring machine (TBM) jamming is one of the main causes that declines the efficiency of excavation rate, and results in construction delay and huge economic loss. A method combining the interpretative structural model (ISM) method and the Bayesian network (BN) is proposed in this study to assess the risk of TBM jamming. The geological disasters directly causing TBM jamming and other key influencing factors are concluded by analyzing 120 engineering cases (detailed information can be found in Appendix). A multi-layer directed node graph indicating the interrelations between the factors is built by the ISM method. And the node graph is used as the structure of BN. Expectation maximization (EM) algorithm is used for parameter learning to obtain the final BN model. Posterior probability analysis shows that tunnel collapse and large deformation of surrounding rock are the main disaster types causing TBM jamming; the rock mass classes, fault zone, and underground water are the main influencing factors. The BN model has been successfully applied to a tunnel in China. It can dynamically predict the probability of geological disasters and TBM jamming according to the geological conditions of tunnel face and the detection results of advance geological prediction. The results of this study can provide some references for the prevention of TBM jamming.
Risk assessment of TBM jamming based on Bayesian networks
https://orcid.org/0000-0002-6578-7583
Abstract Tunnel boring machine (TBM) jamming is one of the main causes that declines the efficiency of excavation rate, and results in construction delay and huge economic loss. A method combining the interpretative structural model (ISM) method and the Bayesian network (BN) is proposed in this study to assess the risk of TBM jamming. The geological disasters directly causing TBM jamming and other key influencing factors are concluded by analyzing 120 engineering cases (detailed information can be found in Appendix). A multi-layer directed node graph indicating the interrelations between the factors is built by the ISM method. And the node graph is used as the structure of BN. Expectation maximization (EM) algorithm is used for parameter learning to obtain the final BN model. Posterior probability analysis shows that tunnel collapse and large deformation of surrounding rock are the main disaster types causing TBM jamming; the rock mass classes, fault zone, and underground water are the main influencing factors. The BN model has been successfully applied to a tunnel in China. It can dynamically predict the probability of geological disasters and TBM jamming according to the geological conditions of tunnel face and the detection results of advance geological prediction. The results of this study can provide some references for the prevention of TBM jamming.
Risk assessment of TBM jamming based on Bayesian networks
Lin, Peng (author) / Xiong, Yue (author) / Xu, Zhenhao (author) / Wang, Wenyang (author) / Shao, Ruiqi (author)
2021
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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