Subway station fires often have serious consequences because of the high density of people and limited number of exits in a relatively enclosed space. In this study, a comprehensive model based on Bayesian network (BN) and the Delphi method is established for the rapid and dynamic assessment of the fire evolution process, and consequences, in underground subway stations. Based on the case studies of typical subway station fire accidents, 28 BN nodes are proposed to represent the evolution process of subway station fires, from causes to consequences. Based on expert knowledge and consistency processing by the Delphi method, the conditional probabilities of child BN nodes are determined. The BN model can quantitatively evaluate the factors influencing fire causes, fire proof/intervention measures, and fire consequences. The results show that the framework, combined with Bayesian network and the Delphi method, is a reliable tool for dynamic assessment of subway station fires. This study could offer insights to a more realistic analysis for emergency decision-making on fire disaster reduction, since the proposed approach could take into account the conditional dependency in the fire propagation process and incorporate fire proof/intervention measures, which is helpful for resilience and sustainability promotion of underground facilities.

Urban underground facilities tend to be vulnerable to flood that is generated by the breaking of a dam or a levee, or a flash flood after an exceptional rainfall. Rapid and dynamic assessment of underground flood evolution process is of great significance for safety evacuation and disaster reduction. Taking advantage of the Delphi method to determine the Bayesian conditional probabilities collected by expert knowledge, this paper proposes an integrated Bayesian Network (BN) framework for rapidly and dynamically assessing the flood evolution process and consequences in underground spaces. The proposed BN framework, including seventeen nodes, can represent the flood disaster drivers, flood disaster bearers, flood mitigation actions, and on-site feedback information. Given evidences to specific nodes, the risk distribution of typical flood scenarios can be quantitatively estimated. The results indicate that the proposed framework can be useful for dynamically evaluating underground flood evolution process and identifying the critical influencing factors. This BN-based framework is helpful for “Scenario-Response”-based predictive analyses to support decision that is related to flood disaster emergency response.