Framework for Probabilistic Vulnerability Analysis of Interdependent Infrastructure Systems

Abstract

Critical infrastructure systems are deteriorating and experiencing increased cascading failures. In this paper, we propose a new probabilistic framework for modeling interdependent infrastructure networks. As part of this framework, we present our modeling approach and the accompanying sets of algorithms that enable the computationally efficient probabilistic modeling of large infrastructure systems considering interdependencies between networks. The proposed method creates a representative Bayesian network (BN) of the system, which provides exact inferences over the network compared to simulation-based approximations. With the traditional computational limitations of BNs to tens of parent component nodes per child system node, our generalized framework enables computationally efficient BN modeling of systems of, for example, hundreds of component nodes, including component-level performance and interdependent connections across networks. Our approach requires only simple inputs based on the basic component characteristics of location, type, connectivity, and initial failure probabilities. We modeled three types of interdependencies across the system-service provision, geographic, and access for repair. We combined a minimum link set (MLS) formulation with the idea of supercomponents in order to reduce network complexity without any approximating assumptions. We present algorithms to efficiently identify MLSs and supercomponents, as well as to identify and remove any cyclic dependencies that arise across the network. Once the BN was constructed, we were able to perform exact inference analyses over a range of component state and hazard event scenarios in order to identify vulnerabilities across the network. The main novelty of the paper is to enable the probabilistic assessment of large, complex, interdependent infrastructure systems. We were able to consider performance from the component level and model hundreds of component nodes in a computationally efficient manner without approximating assumptions. We accounted for interdependencies between systems with exact inference results. The model can be used to investigate the potential for cascading failures and to prioritize critical components for repair, replacement, or reinforcement. We applied the proposed methodology and algorithms to the water distribution network in Atlanta, Georgia, and its dependencies with the power system. We validated the model using the results from a recent interdependent outage event.

On Risk Probability of Prefabricated Building Hoisting Construction Based on Multiple Correlations

“…In terms of study on risk probability, some scholars, from a static point of view, carried out research in situations of multiple risks. Hernandez-Fajardo et al [23] evaluated the risk probability in situations of earthquakes and random failure respectively using a simulation-based method; Applegate et al [24] proposed to evaluate risk probability in situations of multiple disasters using modeling methods and algorithm based on Bayesian network. Other scholars conducted research from the dynamic perspective.…”

Probability Propagation Method for Reliability Assessment of Acyclic Directed Networks

“…In addition, the use of both Bayesian networks and the bound-finding method require identification of the MLS or MCS of a system. Although methods have been proposed to do this efficiently, e.g., the edge cut algorithm, also known as EG-CUT algorithm, to generate MCS (Shin and Koh 1998) and a recursive depth-first search MLS identification algorithm (Applegate and Tien 2019), this is a NP-hard problem (Suh and Chang 2000) for general networks.…”

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

“…This matrix-based BN framework has been further generalized by Byun and Song (2021) to be applied to multi-state systems. Thanks to the decomposition into minimum link sets (MLSs) and the identification of super-components (i.e., sub-sets of components that are only in series or parallel), Applegate and Tien (2019) have also proposed a computationally efficient approach that has the capacity to deal with interdependent infrastructure systems.…”

“…Most of the aforementioned studies have mostly focused on the component-to-system links (e.g., Byun et al 2019;Applegate and Tien 2019;Tien et al 2016), assuming for instance that components are root nodes in the BN, associated with a marginal probability of failure, or that a single root node IM points towards all component nodes. However, in the context of post-earthquake rapid assessment, the accurate consideration of hazardrelated variables leading to component failure probabilities (and their respective correlation structure) is of utmost importance to reach situation awareness.…”