Resilience evaluation of maritime liquid cargo emergency response by integrating FRAM and a BN: A case study of a propylene leakage emergency scenario

Abstract

A novel model is proposed to evaluate the resilience involved in maritime liquid cargo emergency response. The model integrates the superiorities of the function resonance analysis method (FRAM), a directed complex network (CN) and a probabilistic-based Bayesian network (BN) within the resilience engineering framework. The FRAM is employed to qualitatively describe the emergency response process on the basis of function, variability and coupling identification to establish a topological network, which can be considered the prototype of BN and directed CN. The improved K-shell decomposition algorithm is employed to obtain the prior probability of the root nodes based on the developed directed CN, while the conditional probability tables for the BN model simulation are calculated according to the probability distribution of the root nodes. Finally, the developed BN is simulated with AgenaRisk software, and then different emergency scenarios and node sensitivity are investigated. The results show that the proposed probabilistic-based methodology can compensate well for the constraints of the traditional FRAM model by aggregating the quantitative analysis method and can be effectively used to evaluate the resilience involved in the maritime emergency response process. Moreover, the application potential of this methodology can be expanded to other similar industries.