Security risk assessment of submerged floating tunnel based on fault tree and multistate fuzzy Bayesian network

Abstract

To assess the global security risks of the submerged floating tunnel (SFT) in marine environments during operation and provide a basis for risk control, a security risk assessment method using a multistate fuzzy Bayesian network (MFBN) considering complex disaster-inducing factors is proposed. A fault tree model of SFT security risk is established to analyze the causal relationships between global risk and influence factors such as structural components and environmental loads. For root nodes, fuzzy probabilities for each state are obtained through expert knowledge. An improved similarity aggregation method is proposed to integrate expert opinions, mitigating the impact of significant option discrepancies. For non-root nodes, the Leaky Noisy-Max model is used to calculate complex conditional probabilities within the SFT. The probabilities of various security risk states and key risk factors could be determined through reasoning by MFBN. Additionally, a risk prediction method that incorporates domain expert opinions and leverages the BN's ability of updating node probabilities with new information was developed to forecast the security risks of the SFT under wave and current loads.