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Assessment of tunnel face stability subjected to an adjacent tunnel

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  • Feng, Liuyang
  • Zhang, Limao

Abstract

This paper conducts a reliability assessment of the tunnel face stability induced by an adjacent tunnel under incomplete information. The evaluation indicator in this study is the limit support pressure (LSP) for the tunnel excavation face, which is first determined through a detailed finite element analysis. Relying on the numerical results, a hybrid particle swarm optimization-neural network (PSO-NN) is developed to construct the meta-model for LSP. This study develops a framework of Monte Carlo simulation combing with the trivariate copula analysis of the cohesive strength, Poisson ratio, and internal friction angle, to assess the probability distribution of LSP with incomplete information. This study proposes an index α greater than 1.0, to quantify the copula's effect in forty-six illustrative cases. Finally, this paper examines the reliability of different existing formulas of LSP in the tunneling excavation procedure. Rankine and Terzaghi's formulas show a higher reliability value above 0.8. Furthermore, this study proposes a new safety factor for engineering design considering the uncertainties and copula's effect. Through further examining the performance of the new safety factor based on the Rankine and Terzaghi approaches, this study proposes a new procedure to determine LSP for a safe and efficient engineering design practice.

Suggested Citation

  • Feng, Liuyang & Zhang, Limao, 2021. "Assessment of tunnel face stability subjected to an adjacent tunnel," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307286
    DOI: 10.1016/j.ress.2020.107228
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    References listed on IDEAS

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    1. Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
    2. Eryilmaz, Serkan, 2011. "Estimation in coherent reliability systems through copulas," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 564-568.
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    4. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    5. Bensmain, Yassir & Dahane, Mohammed & Bennekrouf, Mohammed & Sari, Zaki, 2019. "Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: A hybrid genetic algorithm based approach," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 546-566.
    6. Yamijala, Shridhar & Guikema, Seth D. & Brumbelow, Kelly, 2009. "Statistical models for the analysis of water distribution system pipe break data," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 282-293.
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    Cited by:

    1. Alibeikloo, Mehrnaz & Khabbaz, Hadi & Fatahi, Behzad, 2022. "Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Ping Liu & Yu Wang & Tongze Han & Jiaming Xu & Qiangnian Li, 2022. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    4. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Qu, Pengfei & Zhang, Limao & Zhu, Qizhi & Wu, Maozhi, 2023. "Probabilistic reliability assessment of twin tunnels considering fluid–solid coupling with physics-guided machine learning," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Tang, Cong & He, Shu-Yu & Zhou, Wan-Huan, 2022. "Settlement-based framework for long-term serviceability assessment of immersed tunnels," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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