HGV fire risk assessment method in highway tunnel based on a Bayesian network

Abstract

The complex characteristics of long-distance highway tunnels may lead to serious loss of life and property in fire incidents. According to the statistical analysis, heavy goods vehicle (HGV) is one of the primary risk factors for tunnel fires. In this study, a quantitative risk assessment model of HGV-involved tunnel fires is established based on the functional resonance analysis method (FRAM) and Bayesian network (BN). Using FRAM, the mechanisms of the incident occurrence and evolution and critical risk factors of HGV-involved tunnel fires are determined, and the BN model is used to quantify the risk based on a probabilistic analysis. Focusing on operational management, monitoring system construction, type of transporting goods, emergency rescue, and egress facilities, the incident severity is determined in terms of casualties and economic losses. The proposed risk assessment method is expected to assist the tunnel operational management and the fire services in identifying critical risk factors in HGV-involved tunnel fires.