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A risk assessment method of deep excavation based on Bayesian analysis and expert elicitation

Author

Listed:
  • Ning Wang

    (Beijing University of Technology
    Shijiazhuang Tiedao University)

  • Cheng-shun Xu

    (Beijing University of Technology)

  • Xiu-li Du

    (Beijing University of Technology)

  • Ming-ju Zhang

    (Beijing University of Technology)

Abstract

In project management, it is an important approach that the deep excavation safety risk (DESR) is synthetically assessed based on experiential information provided by experts. This information is usually obtained from multiple sources and is incomplete. Therefore, the problem of synthesizing multiple sources and uncertainty information is of great significance for the assessment and control of DESR. This paper presents a risk assessment and decision approach for deep excavation construction based on Bayesian analysis. First, the expressions of a likelihood function used to describe and synthesize uncertain information obtained from expert panel is constructed, and a discretization interval division method of occurrence probability and consequences of an unwanted event is proposed based on the current risk code in China. Then, the discrete model of synthesizing information of expert estimates is constructed, and a comparative analysis of expert-elicited results and formula deduction are utilized to evaluate the advantages of this model under some hypothetical scenarios. On that basis, the index system of DESR is established and the weight of each index is calculated by the method of analytic hierarchy process. The risk matrix, deviation degree and risk acceptance criteria are developed to determine the whole risk level. Finally, the proposed method is verified by analyzing a typical deep excavation of Beijing Metro. The results demonstrate the feasibility of this method and its application potential.

Suggested Citation

  • Ning Wang & Cheng-shun Xu & Xiu-li Du & Ming-ju Zhang, 2018. "A risk assessment method of deep excavation based on Bayesian analysis and expert elicitation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(2), pages 452-466, April.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:2:d:10.1007_s13198-017-0689-2
    DOI: 10.1007/s13198-017-0689-2
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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.
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    3. Podofillini, L. & Dang, V.N., 2013. "A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 52-64.
    4. Peter A. Morris, 1977. "Combining Expert Judgments: A Bayesian Approach," Management Science, INFORMS, vol. 23(7), pages 679-693, March.
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    Cited by:

    1. Hadef Hefaidh & Djebabra Mébarek, 2020. "A conceptual framework for risk matrix capitalization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 755-764, June.
    2. Chao Zhang & Wan Wang & Fengjiao Xu & Yong Chen & Tingxin Qin, 2022. "A Risk Treatment Strategy Model for Oil Pipeline Accidents Based on a Bayesian Decision Network Model," IJERPH, MDPI, vol. 19(20), pages 1-16, October.

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