A Dynamic Decision Approach for Risk Analysis in Complex Projects

Underground metro tunnels present a popular solution to relieve the pressure of surface transportation systems worldwide. However, tunnel construction inevitably generates soil displacements and deformations, which may affect the safety performance of the surface road operation. This paper presents a systemic dynamic decision approach based on dynamic Bayesian networks (DBNs), aiming to provide guidelines for the dynamic safety analysis of the tunnel-induced road surface damage over time. The potential uncertainty and randomness underlying tunnel construction is modeled by following a discrete-time Markov chain process. A detailed step-by-step procedure is proposed, including risk/hazard identification, and DBN-based predictive and diagnostic analysis. A case study concerning the dynamic safety analysis in the construction of the Wuhan Yangtze Metro Tunnel is presented. Results demonstrate the feasibility of the proposed approach, as well as its application potential. The relationships between the DNB-based and BN-based approaches are further discussed based on the results. The proposed approach can be used as a decision tool to provide support for safety assurance in tunnel construction, and thus increase the likelihood of a successful project in a dynamic environment.