New framework for probabilistic interdependency modeling and critical component identification to increase infrastructure system resilience
Applegate, Chloe Johansen
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Cascading failures of interdependent infrastructure networks have become increasingly critical as revealed by recent natural disasters and human disruptions. By determining how relationships between different infrastructure systems affect the fragility of those systems, it is proposed in this study that one can identify the most critical components and links, determine which infrastructure components to reinforce, and decrease the time required to regain normal operation. Several infrastructure systems include power, water, natural gas, and transportation. Each of these sectors are integrally tied to one another. This dissertation presents a modeling approach and the accompanying sets of algorithms that enable computationally efficient probabilistic modeling of large infrastructure systems while considering interdependencies between networks. The proposed method creates a computationally tractable, representative Bayesian network of the system, with which exact inferences over the network are possible. Once the Bayesian network is constructed, inference analyses can be performed over a range of component state and hazard event scenarios to identify vulnerabilities across the network. The model is applied to analyze component criticality within the infrastructure systems. Centrality-based and reliability-based component importance measures are considered. The proposed methodology is applied to assess critical water services in the City of Atlanta, Georgia, including dependencies of the water distribution system on the power distribution network.