IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v205y2021ics0951832020307286.html
   My bibliography  Save this article

Assessment of tunnel face stability subjected to an adjacent tunnel

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020307286
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107228?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. 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).
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Xu, Jun & Wang, Ding, 2019. "Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 329-340.
    3. Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
    4. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    5. Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
    6. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. David Breitenmoser & Francesco Cerutti & Gernot Butterweck & Malgorzata Magdalena Kasprzak & Sabine Mayer, 2023. "Emulator-based Bayesian inference on non-proportional scintillation models by compton-edge probing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    8. Sang-Guk Yum & Sungjin Ahn & Junseo Bae & Ji-Myong Kim, 2020. "Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    9. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    10. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    11. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "Real-time high-fidelity reliability updating with equality information using adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    12. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    15. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
    16. Al Ali, Hannah & Daneshkhah, Alireza & Boutayeb, Abdesslam & Malunguza, Noble Jahalamajaha & Mukandavire, Zindoga, 2022. "Exploring dynamical properties of a Type 1 diabetes model using sensitivity approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 324-342.
    17. Yu, Shui & Wang, Zhonglai & Zhang, Kewang, 2018. "Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 45-52.
    18. Gersbach, Hans & Liu, Yulin & Tischhauser, Martin, 2021. "Versatile forward guidance: escaping or switching?," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    19. Rasoul Amirzadeh & Asef Nazari & Dhananjay Thiruvady & Mong Shan Ee, 2023. "Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks," Papers 2306.08157, arXiv.org.
    20. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307286. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.