The growing risk of natural and artificial or manufactured hazards combined with a lack of community preparedness have revealed the necessity for comprehensive and effective metrics for evaluating and improving a community’s resilience, i.e., the ability of communities to prepare for, withstand, and recover from disasters. In this paper, the authors review existing community resilience metrics and tools, classifying them into one of three main categories: community-level, sector-specific, or sociological. The paper provides short descriptions of each metric and comparisons across metrics within the three main categories and across classes. The authors assess the strengths and limitations of these metrics, discuss challenges in improving community resilience, and provide recommendations for the development of new measures of resilience. The paper concludes with an outlook on the future of community resilience, particularly the need for metrics that apply across hazards, geographic areas, and factor...

The authors present a Bayesian network (BN)-based approach for modeling and reliability assessment of infrastructure systems. The BN is a powerful framework that is able to account for uncertainties in component and system parameters, and perform updating of system assessments with new information. The exponential increase in memory storage required for the BN model as the size of the system increases has limited the applicability of BNs for reliability assessment of large infrastructure systems. Recently, a data-compression method was proposed to address this limitation. While significantly reducing the memory storage, computational time for constructing the BN and performing inference increased. In this paper, new methodologies are developed to increase the computational efficiency of a compression-based approach for BN modeling and reliability assessment of infrastructure systems. These include algorithms to improve the computational efficiency of the initial compression for constructing the BN, subsequent inference over the network, and overall system formulation. The algorithms are applied to a test example system to examine their performance for systems of increasing size, as well as to a 59-component power distribution network to demonstrate application to real systems. Performance of the proposed methodologies is compared to that of an existing, widely used BN algorithm. With the heuristics employed, the new algorithms are shown to achieve significant gains in both memory storage and computation time, enabling the modeling of large infrastructure systems as BNs for system reliability analysis.

The Bayesian network (BN) is a useful tool for the modeling and reliability assessment of civil infrastructure systems. For a system comprising many interconnected components, it captures the probabilistic dependencies between components and system performance, with inference in the BN informing decision making in the management of these systems. However, one of the major challenges in the BN modeling of infrastructure systems is the exponentially increasing computational complexity as the number of components in the system increases. Previously, algorithms have been developed for BN modeling of binary systems. Compared with binary systems, multistate system modeling provides a more detailed description of system reliability and enables the analysis of flow instead of connectivity networks. However, the dimensionality of the problem also increases. This paper advances the state of the art in BN modeling of complex networks by presenting new algorithms for constructing the BN model for multistate components and systems and performing exact inference over these models. The results support reliability assessment of civil infrastructure flow systems. Specifically, the authors present a new lossless compression algorithm for initial construction of the BN model and simultaneous preprocessing of intermediate factors for inference. These significantly reduce memory storage requirements for the BN. Two heuristics are described to further increase computational efficiency. The new algorithms are applied to an example infrastructure system. The ability to conduct inference across the network is demonstrated and performance measured compared to existing algorithms in terms of both memory storage and computation time. The proposed algorithms are shown to achieve exponentially increasing data compression with a stable increased computation time ratio, enabling larger multistate flow networks to be modeled as BNs than previously possible.

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.