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Bayesian-network-based safety risk analysis in construction projects

Author

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  • Zhang, Limao
  • Wu, Xianguo
  • Skibniewski, Miroslaw J.
  • Zhong, Jingbing
  • Lu, Yujie

Abstract

This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion†is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:131:y:2014:i:c:p:29-39
    DOI: 10.1016/j.ress.2014.06.006
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    10. 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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    15. Albert P. C. Chan & Francis K. W. Wong & Carol K. H. Hon & Tracy N. Y. Choi, 2018. "A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work," IJERPH, MDPI, vol. 15(11), pages 1-19, November.
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    18. Zhan-Sheng Liu & Xin-Tong Meng & Ze-Zhong Xing & Cun-Fa Cao & Yue-Yue Jiao & An-Xiu Li, 2022. "Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    19. 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).
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    21. Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
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