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Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis

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  • Wang, Fan
  • Li, Heng
  • Dong, Chao
  • Ding, Lieyun

Abstract

Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs) are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal relationships, and update models when evidence is available. However, the model development based on classic BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pair-wise Pearson's linear correlations to parameterize the model because the assessment is intuitive and experts in the engineering domain are more familiar and comfortable with this notion. However, when Spearman's rank correlation is given, the method can also be used by modification of the marginals. The method is illustrated with a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential applications of the model are also explored, such as decision support for risk-based design.

Suggested Citation

  • Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018315710
    DOI: 10.1016/j.ress.2019.106529
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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. 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.
    3. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
    4. Morales, O. & Kurowicka, D. & Roelen, A., 2008. "Eliciting conditional and unconditional rank correlations from conditional probabilities," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 699-710.
    5. Langseth, Helge & Nielsen, Thomas D. & Rumí, Rafael & Salmerón, Antonio, 2009. "Inference in hybrid Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1499-1509.
    6. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    7. Lee, Dongjin & Pan, Rong, 2018. "A nonparametric Bayesian network approach to assessing system reliability at early design stages," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 57-66.
    8. Sýkora, Miroslav & Marková, Jana & Diamantidis, Dimitris, 2018. "Bayesian network application for the risk assessment of existing energy production units," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 312-320.
    9. Hanea, Anca & Morales Napoles, Oswaldo & Ababei, Dan, 2015. "Non-parametric Bayesian networks: Improving theory and reviewing applications," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 265-284.
    10. 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.
    11. Limao Zhang & Xianguo Wu & Yawei Qin & Miroslaw J. Skibniewski & Wenli Liu, 2016. "Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 278-301, February.
    12. Cooke, Roger M. & Goossens, Louis L.H.J., 2008. "TU Delft expert judgment data base," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 657-674.
    13. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    14. Robert T. Clemen & Gregory W. Fischer & Robert L. Winkler, 2000. "Assessing Dependence: Some Experimental Results," Management Science, INFORMS, vol. 46(8), pages 1100-1115, August.
    15. Zhou, Ying & Li, Chenshuang & Zhou, Cheng & Luo, Hanbin, 2018. "Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 152-167.
    16. Argenti, Francesca & Landucci, Gabriele & Reniers, Genserik & Cozzani, Valerio, 2018. "Vulnerability assessment of chemical facilities to intentional attacks based on Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 515-530.
    17. Ibsen Chivatá Cárdenas & Saad S.H. Al‐jibouri & Johannes I.M. Halman & Frits A. van Tol, 2013. "Capturing and Integrating Knowledge for Managing Risks in Tunnel Works," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 92-108, January.
    18. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    19. Ibsen Chivatá Cárdenas & Saad S. H. Al‐Jibouri & Johannes I. M. Halman & Wim van de Linde & Frank Kaalberg, 2014. "Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1923-1943, October.
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    2. Wang, Ying & Zhang, Limao, 2021. "Simulation-based optimization for modeling and mitigating tunnel-induced damages," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

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