A dynamic Bayesian network based approach to safety decision support in tunnel construction. (February 2015)

• Record Type: Journal Article
• Authors: Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying
• Is Part Of: Reliability engineering & system safety. Volume 134(2015:Feb.)
• Page Start: 157
• Page End: 168
• Publication Date: 2015-02
• Subjects: Construction safety -- Dynamic Bayesian network (DBN) -- Predictive analysis -- Sensitivity analysis -- Diagnostic analysis -- Tunnel construction; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2014.10.021
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. Highlights: A dynamic Bayesian network (DBN) based approach for safety decision support is developed. This approach is able to perform feed-forward, concurrent and back-forward analysis and control. A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. DBN-based approach can perform a higher accuracy than traditional static BN-based approach.