IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v241y2024ics0951832023005732.html
   My bibliography  Save this article

Causal discovery and reasoning for geotechnical risk analysis

Author

Listed:
  • Liu, Wenli
  • Liu, Fenghua
  • Fang, Weili
  • Love, Peter E.D.

Abstract

Artificial intelligence (AI), such as machine learning (ML) models, is profoundly impacting an organization's ability to assess safety risks during the construction of tunnels. Yet, ML models are black boxes and suffer from interpretability and transparency issues – they are unexplainable. Hence the motivation of this paper is to address the following research question: How can we effextively explain data-driven ML model's predicitve assessment of geotechnical risks in tunnel construction? We draw on the concept of ‘eXplainable AI’ (XAI) and utilize causal discovery and reasoning to help analyze and interpret the manifestation of geotechnical risks in tunnel construction by developing: (1) a sparse nonparametric and nonlinear directed acyclic diagram (DAG) used to determine the causal structure of risks between sub-systems; (2) a multiple linear regression model, which we use to estimate the effect of the causal relationships between sub-systems; and (3) a probability-based reasoning model to quantify and reason about risk. We use the San-yang Road tunnel project in Wuhan (China) to validate the feasibility and effectiveness of our proposed approach. The results indicate that our approach can accurately explain what and how risks are obtained from a data-driven probability-based ML model for ground settlement in tunnel construction.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005732
    DOI: 10.1016/j.ress.2023.109659
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023005732
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109659?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. Lin, Penghui & Zhang, Limao & Tiong, Robert L.K., 2023. "Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. 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).
    4. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    8. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    9. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    10. He, Jingran & Gao, Ruofan & Chen, Jianbing, 2022. "A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Ali Behravan & Bahareh Kiamanesh & Roman Obermaisser, 2021. "Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities," Energies, MDPI, vol. 14(20), pages 1-47, October.
    12. 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).
    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. Zhang, Bohua & Wang, Weigang & Lei, Haoran & Hu, Xiancun & Li, Chun-Qing, 2024. "An improved analytical solution to outcrossing rate for scalar nonstationary and non-gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(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. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Mathpati, Yogesh Chandrakant & More, Kalpesh Sanjay & Tripura, Tapas & Nayek, Rajdip & Chakraborty, Souvik, 2023. "MAntRA: A framework for model agnostic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Oh, ChoHwan & Lee, Jeong Ik, 2020. "Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    5. Ssembatya, Martin & Claridge, David E., 2024. "Quantitative fault detection and diagnosis methods for vapour compression chillers: Exploring the potential for field-implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    6. Li, Bingxu & Cheng, Fanyong & Zhang, Xin & Cui, Can & Cai, Wenjian, 2021. "A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data," Applied Energy, Elsevier, vol. 285(C).
    7. Wang, Fanyi & Ma, Wanying & Mirza, Nawazish & Altuntaş, Mehmet, 2023. "Green financing, financial uncertainty, geopolitical risk, and oil prices volatility," Resources Policy, Elsevier, vol. 83(C).
    8. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    9. Chang, C-L. & McAleer, M.J. & Wang, Y-A., 2018. "Latent Volatility Granger Causality and Spillovers in Renewable Energy and Crude Oil ETFs," Econometric Institute Research Papers TI 2018-052/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Yuan, Di & Li, Sufang & Li, Rong & Zhang, Feipeng, 2022. "Economic policy uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis," Energy Economics, Elsevier, vol. 110(C).
    12. Zhang, Hao & Cai, Guixin & Yang, Dongxiao, 2020. "The impact of oil price shocks on clean energy stocks: Fresh evidence from multi-scale perspective," Energy, Elsevier, vol. 196(C).
    13. Zhou, Wuhao & Xu, Yuanlu & Zhang, Li & Lin, Huifang, 2023. "Does public behavior and research development matters for economic growth in SMEs: Evidence from Chinese listed firms," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 107-119.
    14. Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. 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.
    16. Cai, Yifei & Chang, Hao-Wen & Xiang, Feiyun & Chang, Tsangyao, 2023. "Can precious metals hedge the risks of Sino–US political relation?–Evidence from Toda–Yamamoto causality test in quantiles," Finance Research Letters, Elsevier, vol. 58(PA).
    17. Muhammad Haseeb & Irwan Shah Zainal Abidin & Qazi Muhammad Adnan Hye & Nira Hariyatie Hartani, 2019. "The Impact of Renewable Energy on Economic Well-Being of Malaysia: Fresh Evidence from Auto Regressive Distributed Lag Bound Testing Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 269-275.
    18. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    19. Antonio Rosato & Francesco Guarino & Mohammad El Youssef & Alfonso Capozzoli & Massimiliano Masullo & Luigi Maffei, 2022. "Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Cond," Energies, MDPI, vol. 15(18), pages 1-38, September.
    20. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(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:241:y:2024:i:c:s0951832023005732. 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.