A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port

•A framework for designing electrical system and its interdependent network (EIN) is proposed.•The underlying factors of EIN are classified with respect to different types of capacities.•A probabilistic graphical model; Bayesian network, is developed to assess the resilience of EIN.•Different analyses are conducted to provide a better insight regarding the result of the model. Ports are an integral part of the transportation system and are often susceptible to a diverse range of risks, including natural disasters, malicious cyber-attacks, technological factors, organizational factors, economic factors, and human error. To address the challenges triggered by these diverse risks, this research identifies the basic factors that could enhance the resilience of the port system. After these factors are identified and expressed as different resilience capacities, they are used to quantify the resilience of the port infrastructure by applying a Bayesian network. Quantification of resilience is further analyzed based on different advanced techniques such as forward propagation, backward propagation, sensitivity analysis, and information theory. The formal interpretation of these analyses indicates that maintenance, alternate routing, and manpower restoration are the leading factors contributing to enhancing the resilience of a port infrastructure system under disruptive conditions.