Assessment of port resilience based on Evidential Reasoning and Bayesian network: An improved framework by segmenting the metrics across time and performance dimensions

Ports are increasingly vulnerable to various disruptions created by natural disasters and volatile socioeconomic conditions. This paper aims to develop measures to enhance port resilience against risks and uncertainties. A five-stage approach is proposed to study port resilience. The major disturbances affecting the port are identified. An improved hierarchical structure is then designed to divide resilience into preparedness, response, and recovery capabilities. Here, after the key metrics of resilience are determined by segmenting the evolution process of resilience, they are used to assess different resilience capabilities and categorize strategies. An evidence fusion–Bayesian network model is proposed, employing D-S evidence theory to fuse prior probability information from different experts. Finally, a large container port and bulk cargo port in China are used as case studies. The quantification of resilience is further analyzed based on sensitivity analysis, forward propagation, and backward propagation, identifying the key strategies and pathways for enhancing resilience. Validation of expert empirical data and the network model is conducted using a dataset generated based on the Success Likelihood Index Methodology(SLIM). The results show that the resilience of container terminals is generally higher than that of bulk terminals. Enhancing resilience requires a comprehensive consideration of capabilities, metrics, and strategies.