A Risk Analysis Method of Cable Fire in Utility Tunnel Based on a Bayesian Network

Abstract

Recently, the fire safety of tunnels is attracting more and more attention with the utility tunnel springing up in major cities of China. Faced with challenges from fires in utility tunnels, fire risk analysis is critical and essential to discover the weaknesses of risk. It serves as the foundation of regulatory decision-making on whether to take appropriate measures to reduce risk. This study combines a Bayesian network (BN) model and a bow-tie (BT) method to propose a fire risk analysis method to predict the probability of cable fire in utility tunnels. First, cable fire risk factors and five potential accident scenarios are analyzed by BT method; Second, to avoid the influence of uncertain factors in BT, the optimized BN model is applied to the prediction analysis of cable fire probability, which can consider the actual development of cable fire in utility tunnel. This novel approach can better reveal the causal relationship between events and determine the critical basic events by sensitivity analysis. Finally, the probability of cable fire ignition occurrence and scenarios probabilities are periodically updated using the cumulative information collected during a time interval. The results show that this approach can assist in dealing with the uncertainty problem in utility tunnel and the optimized model is more in line with the reality by comparing the results before model optimization.

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