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Prediction of the Tunnel Collapse Probability Using SVR-Based Monte Carlo Simulation: A Case Study

Author

Listed:
  • Guowang Meng

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China
    State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures, Guangxi University, Nanning 530004, China)

  • Hongle Li

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China
    State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures, Guangxi University, Nanning 530004, China)

  • Bo Wu

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China
    School of Civil and Architectural Engineering, East China University of Technology, Nanchang 330013, China)

  • Guangyang Liu

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China
    State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures, Guangxi University, Nanning 530004, China)

  • Huazheng Ye

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China)

  • Yiming Zuo

    (School of Civil Engineering and Architecture, Guangxi University, 100 University Road, Nanning 530004, China)

Abstract

Collapse is one of the most significant geological hazards in mountain tunnel construction, and it is crucial to accurately predict the collapse probability. By introducing the reliability theory, this paper proposes a calculation method for the collapse probability in mountain tunnel construction based on numerical simulation, support vector regression (SVR), and the Monte Carlo (MC) method. Taking the Jinzhupa Tunnel Project in Fujian Province as a case study, three-dimensional models were constructed, and the safety factors of the surrounding rock were determined using the strength reduction method. By defining the shear strength parameters of the surrounding rock as random variables, the problem was formulated as a reliability model, and the safety factor was chosen as the reliability index. To increase computational efficiency, the SVR model was trained to replace numerical simulations, and the MC method was adopted to calculate the probability of collapse. The results showed that the cause of the collapse was the change in the excavation method and the very late installation of supports. The feasibility and reliability of the proposed method have been verified, indicating that the method can be used to predict the probability of collapse in a practical risk assessment of mountain tunnel construction.

Suggested Citation

  • Guowang Meng & Hongle Li & Bo Wu & Guangyang Liu & Huazheng Ye & Yiming Zuo, 2023. "Prediction of the Tunnel Collapse Probability Using SVR-Based Monte Carlo Simulation: A Case Study," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7098-:d:1131145
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    References listed on IDEAS

    as
    1. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    2. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    3. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, March.
    4. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
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    Cited by:

    1. Chengkun Wang & Zhengyu Liu & Fengkai Zhang & Qian Guo & Zhao Dong & Peng Bai, 2024. "Heat Hazards in High-Temperature Tunnels: Influencing Factors, Disaster Forms, the Geogenetic Model and a Case Study of a Tunnel in Southwest China," Sustainability, MDPI, vol. 16(3), pages 1-17, January.

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