IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v231y2023ics0951832022006007.html
   My bibliography  Save this article

A hybrid data-driven model for geotechnical reliability analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022006007
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108985?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. Yong, Weixun & Zhang, Wengang & Nguyen, Hoang & Bui, Xuan-Nam & Choi, Yosoon & Nguyen-Thoi, Trung & Zhou, Jian & Tran, Trung Tin, 2022. "Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    4. Hesabi, Hadis & Nourelfath, Mustapha & Hajji, Adnène, 2022. "A deep learning predictive model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. 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).
    6. 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).
    7. Zhou, Ying & Li, Chenshuang & Ding, Lieyun & Sekula, Przemyslaw & Love, Peter E.D. & Zhou, Cheng, 2019. "Combining association rules mining with complex networks to monitor coupled risks," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 194-208.
    8. Zhang, Limao & Wu, Xianguo & Skibniewski, Miroslaw J. & Zhong, Jingbing & Lu, Yujie, 2014. "Bayesian-network-based safety risk analysis in construction projects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 29-39.
    9. Liu, Wenli & Chen, Elton J. & Yao, Erlei & Wang, Yanyu & Chen, Yangyang, 2021. "Reliability analysis of face stability for tunnel excavation in a dependent system," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    10. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Li, Peiping & Wang, Yu, 2022. "An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. P E D Love & C-P Sing & X Wang & D J Edwards & H Odeyinka, 2013. "Probability distribution fitting of schedule overruns in construction projects," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(8), pages 1231-1247, August.
    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. Jerez, Danko J. & Chwała, M. & Jensen, Hector A. & Beer, Michael, 2024. "Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    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).

    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. Jiadong, Qiu & Ohl, Joy P. & Tran, Trung-Tin, 2024. "Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Shen, Shui-Long & Lin, Song-Shun & Zhou, Annan, 2023. "A cloud model-based approach for risk analysis of excavation system," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. 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).
    4. Pan, Yue & Qin, Jianjun & Hou, Yongmao & Chen, Jin-Jian, 2024. "Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Oluwatuyi, Opeyemi E. & Ng, Kam & Wulff, Shaun S., 2023. "Improved resistance prediction and reliability for bridge pile foundation in shales through optimal site investigation plans," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Shirgir, Sina & Shamsaddinlou, Amir & Zare, Reza Najafi & Zehtabiyan, Sorour & Bonab, Masoud Hajialilue, 2023. "An efficient double-loop reliability-based optimization with metaheuristic algorithms to design soil nail walls under uncertain condition," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Yong, Weixun & Zhang, Wengang & Nguyen, Hoang & Bui, Xuan-Nam & Choi, Yosoon & Nguyen-Thoi, Trung & Zhou, Jian & Tran, Trung Tin, 2022. "Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Luo, Changqi & Zhu, Shun-Peng & Keshtegar, Behrooz & Niu, Xiaopeng & Taylan, Osman, 2023. "An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Ming Fang & Yi Zhang & Mengjue Zhu & Shaopei Chen, 2022. "Cause Mechanism of Metro Collapse Accident Based on Risk Coupling," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    11. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    12. Gaogeng Zhu & Guoming Chen & Jingyu Zhu & Xiangkun Meng & Xinhong Li, 2022. "Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry," IJERPH, MDPI, vol. 19(12), pages 1-27, June.
    13. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
    14. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    15. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    16. Li, Yaping & Xia, Tangbin & Chen, Zhen & Pan, Ershun, 2023. "Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. Joana Fernandes & Maria Catarina Santos & Rui Castro, 2021. "Introductory Review of Energy Efficiency in Buildings Retrofits," Energies, MDPI, vol. 14(23), pages 1-18, December.
    18. Mu, Yunfei & Xu, Yanze & Zhang, Jiarui & Wu, Zeqing & Jia, Hongjie & Jin, Xiaolong & Qi, Yan, 2023. "A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems," Applied Energy, Elsevier, vol. 346(C).
    19. Guo, Qingjun & Amin, Shohel & Hao, Qianwen & Haas, Olivier, 2020. "Resilience assessment of safety system at subway construction sites applying analytic network process and extension cloud models," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    20. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    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:231:y:2023:i:c:s0951832022006007. 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.