Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports

Abstract

Infrastructure systems, including transportation, telecommunications, water supply, and electric power networks, are faced with growing number of disruptions such as natural disasters, malevolent attacks, human-made accidents, and common failures, due to their age, condition, and interdependence with other infrastructures. Risk planners, previously concerned with protection and prevention, are now more interested in the ability of such infrastructures to withstand and recover from disruptions in the form of resilience building strategies. This paper offers a means to quantify resilience as a function of absorptive, adaptive, and restorative capacities with Bayesian networks. A popular tool to structure relationships among several variables, the Bayesian network model allows for the analysis of different resilience building strategies through forward and backward propagation. The use of Bayesian networks to quantify resilience is demonstrated with the example of an inland waterway port, an important component in the intermodal transportation network.