Abstract
In order to comprehensively understand the internal operation mechanism and external interaction relationship during the navigation of coupled systems such as Liquefied Natural Gas-Fueled Vessels (LNGFV),this paper proposes a hybrid model that integrates the Causal Analysis based on System Theory (CAST) with Dynamic Bayesian Networks (DBN) to analyze the traffic resilience of LNGFV during navigation. Starting from the mechanism of system resilience, the study constructs a resilience evolution framework for LNGFV based on the Triple Protection Mechanism (TPM). The CAST method is employed to identify key influencing factors within the navigation safety control structure, and the integration of DBN with Hidden Markov Models (HMM) is used to quantify the dynamic characteristics of resilience. Through the analysis of LNGFV accident cases and environment data-driven simulations, the results indicate that the evolution of LNGFV traffic resilience follows a double U-shaped trend and is significantly positively correlated with the implementation effectiveness of TPM, demonstrating TPM's capability to maintain high resilience levels under external disturbances. This framework provides a systematic method and a new perspective for resilience assessment in complex shipping systems, supporting optimized decision-making for multi-level risk management.
