Improving tunnel fire safety: a Bayesian network model for risk analysis and a novel risk acceptability range for risk evaluation

This is the authors' abstract. We don't add key points for this paper.

Event Tree Analysis (ETA) and FN curves are widely used for fire risk analysis and evaluation. However, both have been criticised for their drawbacks. This study addresses these gaps by proposing a Bayesian Network (BN) risk analysis model using fire incident data in Australia, which has never been explored before. The BN model is developed using a score-based approach and validated using four indicators. The innovation of this paper is the introduction of a novel risk acceptability range as a risk evaluation method by integrating BN and Causal Network Topology Analysis (CaNeTA). CaNeTA identifies critical variables through network robustness analysis, which are then used to test sensitivity against current average performance. The results highlight the importance of a rapid and efficient response to fire incidents in road tunnels in Australia. The risk acceptability range represents the current average performance of road tunnels based on actual incidents in road tunnels in Australia. This range is an alternative risk evaluation tool in the absence of specific acceptance criteria. It provides an alternative perspective on risk acceptability in the absence of fatality data. In addition, the process of converting a BN model into CaNeTA provides valuable insights into causal network analysis.