Journal
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
Volume 140, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tust.2023.105247
Keywords
Heavy goods vehicle fire; Long-distance highway tunnel; Risk assessment; Functional resonance analysis method; Bayesian network
Ask authors/readers for more resources
A quantitative risk assessment model of HGV-involved tunnel fires is established using the FRAM and BN methods, helping to identify critical risk factors and assist in tunnel management and firefighting.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available