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A hybrid data-driven model for geotechnical reliability analysis

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  • Liu, Wenli
  • Li, Ang
  • Fang, Weili
  • Love, Peter E.D.
  • Hartmann, Timo
  • Luo, Hanbin

Abstract

Tunnel boring machines are widely used to construct underground rail networks in urban areas. However, ground settlement due to complex geological conditions is an ever-present reality requiring continuous monitoring and management of risks. This paper addresses the following research question: How can we predict tunnel-induced ground settlement with engineering parameters, improve its predictive ability, and quantify its risks under uncertain parameters in complex geological conditions? To this end, we develop a hybrid data-driven model that considers prior domain knowledge to effectively and accurately quantify risk under uncertain parameters during a tunnel's excavation process. Our model comprises: (1) a deep neural network (DNN) to construct a ground settlement prediction model; (2) the incorporation of physical knowledge into the DNN-based prediction model; and (3) a Markov-chain-based importance sampling to analyze settlement reliability. We use the San-yang Road tunnel project in Wuhan, China, to evaluate the effectiveness and feasibility of our proposed approach. The results demonstrate that our hybrid data-driven model can accurately predict tunnel-induced ground settlement and quantify failure probability for geotechnical reliability under uncertain parameters during a tunnel's excavation process.

Suggested Citation

  • Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006007
    DOI: 10.1016/j.ress.2022.108985
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    Cited by:

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    2. Liu, Wenli & Liu, Fenghua & Fang, Weili & Love, Peter E.D., 2024. "Causal discovery and reasoning for geotechnical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Guo, Tiexin & Wang, Hongji & Li, Jinglai & Wang, Hongqiao, 2024. "Sampling-based adaptive design strategy for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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