Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network

Jiansong Wu,Zhuqiang Hu,Jinyue Chen,Zheng Li

doi:10.3390/su10103810

Abstract

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.

Highlights

The urgent demands for land resources in urban development, more and more underground spaces, such as subway stations, underground malls, parking lots, and so on, are explored and constructed
It is of great importance to carry out disaster prevention and emergency response work in subway stations, especially on the risk assessment of fire disaster in the subway stations, which is of great significance for urbanization and sustainability
Some researchers recently have been working on rapid risk assessment tool for subway station fires from qualitative and quantitative perspectives, based on probabilistic methods and stochastic approaches, like event tree analysis, fuzzy fault tree analysis, cluster analysis, failure modes and effects analysis, optimized neural network, analytic hierarchy process, grey-analytic hierarchy process, etc. [14,15,16,17,18,19,20]

Summary

Introduction

The urgent demands for land resources in urban development, more and more underground spaces, such as subway stations, underground malls, parking lots, and so on, are explored and constructed. The CFD-based numerical results on subway station fire characteristics and development process are of significance for making “Preparedness” strategies for fire disaster, e.g., safety evacuation design and fire prevention measures in subway stations. Some researchers recently have been working on rapid risk assessment tool for subway station fires from qualitative and quantitative perspectives, based on probabilistic methods and stochastic approaches, like event tree analysis, fuzzy fault tree analysis, cluster analysis, failure modes and effects analysis, optimized neural network, analytic hierarchy process, grey-analytic hierarchy process, etc. Zheng et al carried out fire risk assessment of a highway tunnel from three levels through a grey-analytic hierarchy process [19] These traditional risk analysis methods, like fuzzy fault tree analysis and event tree analysis, can be very large for complex systems, which brings difficulties to qualitative and quantitative analysis. Some researchers have achieved a lot on the risk analysis of other crowded places with potential fire risk, e.g., finding out the types of bridges most susceptible to fire by statistical analysis of a large number of historical data [21], and proposing a way of weighted factors to quantify bridge fire risk [22]

Methods

Results

Conclusion