Risk Analysis of Construction with Tunnel Boring Machines Passing Under Existing Tunnels Based on Gaussian Copula Bayesian Network Model

WU Zhongtan

doi:10.13204/j.gyjzG22103112

Volume 53 Issue 11

Nov. 2023

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WU Zhongtan. Risk Analysis of Construction with Tunnel Boring Machines Passing Under Existing Tunnels Based on Gaussian Copula Bayesian Network Model[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(11): 55-64. doi: 10.13204/j.gyjzG22103112

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WU Zhongtan. Risk Analysis of Construction with Tunnel Boring Machines Passing Under Existing Tunnels Based on Gaussian Copula Bayesian Network Model[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(11): 55-64. doi: 10.13204/j.gyjzG22103112

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Risk Analysis of Construction with Tunnel Boring Machines Passing Under Existing Tunnels Based on Gaussian Copula Bayesian Network Model

doi: 10.13204/j.gyjzG22103112

WU Zhongtan

Wuhan Metro Group Co., Ltd., Wuhan 430030, China

Received Date: 2022-10-31

Abstract

Abstract

To analyze, manage and control safety risks of construction with tunnel boring machines passing under existing tunnels, a risk analysis method based on the Gaussian Copula Bayesian Network (GCBN) model was proposed. Based on the fault tree, a set of the construction safety risk index system including 12 factors was established, and the dynamic inferential diagnosis of Bayesian Network was combined with the dependence expression of Copula Theory. The GCBN model for risk analysis of construction with tunnel boring machines passing under existing tunnels was constructed in the condition of uncertain and incomplete information. Taking the project of Wuhan Rail Transit Line 12 passing under the existing Line 7 as an example, the Gaussian Copula was used to identify marginal distribution types of each factor, calculate correlation coefficients between each factor, and connect nodes in the network. Through the model reasoning, qualitative analysis and quantitative analysis were conducted, the safety state under construction with tunnel boring machines passing under existing tunnels could be identified, the impact the risk factors on results of risks was analyzed. Eventually, preventive and control measures for high sensitivity factors were implemented. By comparison with the calculating results of models before and after being prevented and controlled, the real-time dynamic security warning and control for construction with tummel boring machines passing under existing tunnels were achieved. The application results indicated that the prediction results by the GCBN model were consistent with the evaluation results by experts, which verified the reliability of the established risk analysis model of GCBN.

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References

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