An integrated framework for managing fire resilience of metro station system: Identification, assessment, and optimization. (July 2022) - Tang, Yuchun; Bi, Wei; Varga, Liz; Dolan, Tom; Li, Qiming
Abstract: As a sociotechnical infrastructure system constituting equipment and facilities, operational staff, and passengers, the metro station system (MSS) is under fire threat from varying causes. Although fires occupy a high portion of all hazards occurring in the MSS, how the MSS in operation can systematically cope with fires has drawn scant attention. To improve MSSs' poor performance across the fire management cycle, the concept of fire resilience is proposed based on the system resilience theory. The disaster scene analysis, TOSE approach, and modified TOPSIS method are combined to identify fire resilience indexes. Then, a Bayesian network is developed to assess fire resilience and reveal critical causal chain in fire scenes. Furthermore, sensitivity analysis and dynamic Bayesian network with critical importance analysis are adopted to formulate optimization strategies for different periods of operating life. The resulting framework is applied to Nanjing MSS, providing operational staff and decision makers with practical tools to engage in long-term resilient operation of MSS against fires within a clear manageable scope. The results indicate passengers' escape skills and safety behaviors, security screening operations, equipment inspection and maintenance, and rescue service access are the prime factors resulting in low fire resilience; meanwhile, economic resource allocation should be prioritized for optimization initially, but optimization priorities should be transferred to the less controllable passengers' escape skills and aging firefighting equipment as operating life increases. The integration of identification, assessment, and optimization methods can also be flexibly embedded into various infrastructure systems' operation management processes to optimize disaster resilience continuously. Highlights: A D-TOSE model is proposed to identify fire resilience indexes for metro station system. A Bayesian network is constructed to simulate the formation and emergence of fire resilience. Critical importance analysis is applied for dynamic optimization of fire resilience. The proposed framework is tested with a real-world case through investigating experts.