Criticality and Susceptibility Indexes for Resilience-Based Ranking and Prioritization of Components in Interdependent Infrastructure Networks

Balakrishnan, Srijith; Zhang, Zhanmin

doi:10.1061/(asce)me.1943-5479.0000769

Cited by 19 publications

(12 citation statements)

References 52 publications

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“…To mitigate the negative effects of high dimensionality, our study exploits clustering methods to identify similar infrastructure components in terms of their topological and functional properties and later incorporates that information to enhance the accuracy of resilience prediction models. Several studies in the literature have demonstrated that topological and functional attributes of components influence the infrastructure network vulnerability and resilience characteristics [22,23,24].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have demonstrated that centrality measures [22,33,24], such as degree centrality, betweenness centrality, and eigenvector centrality, could capture the vulnerability and resilience of infrastructure network components. In addition, many studies have shown that specific functional properties, such as flow rates under normal operating conditions, could serve as indicators of the vulnerability and importance of infrastructure components in a system [23].…”

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confidence: 99%

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Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Balakrishnan¹,

Cassottana²,

Verma³

2022

Preprint

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Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in a large number of features and cause ML models to suffer from the 'curse of dimensionality'. We present a clustering-based method that simultaneously minimizes the problem of high-dimensionality and improves the prediction accuracy of ML models developed for resilience analysis in large-scale interdependent infrastructure networks. The methodology has three parts: (a) generation of simulation dataset, (b) network component clustering, and (c) dimensionality reduction and development of prediction models. First, an interdependent infrastructure simulation model simulates the network-wide consequences of various disruptive events. The component-level features are extracted from the simulated data. Next, clustering algorithms

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Balakrishnan¹,

Cassottana²,

Verma³

2022

Preprint

Self Cite

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“…Almoghathawi and Barker 28 developed two component importance measures for interdependent networks to find key components. Balakrishnan and Zhang 29 proposed two resilience indexes, a node criticality index and a node susceptibility index, to deal with two types of node ranking relevant to infrastructure network resilience. Fang et al 30 proposed the optimal repair time and the resilience reduction worth to measure the criticality of the components of the system.…”

Section: Introductionmentioning

confidence: 99%

Importance measure-based resilience analysis of a wind power generation system

Dui

Xiang

Guo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Different from other forms of power generation, wind power generation has the characteristics of randomness, intermittentness, and volatility. Therefore, the wind power generation system (WPGS) is more prone to failures caused by external impacts during the operation. The reliability of the WPGS has a significant influence on the safety of large-scale power grid systems. Thus, how to accurately evaluate the reliability of the WPGS that is integrated into large-scale power grid systems has become a new challenge. Implementing effective resilience management in WPGSs can improve their ability to handle interruptions and increase the safety of the grid-connected system. This research proposes a new method of resilience management based on importance measure for the WPGS after multi-node failures due to natural disasters or man-made accidents. Firstly, both the performance of the WPGS before multi-node failures and the performance of the WPGS after multiple nodes failures are analyzed. Then, the performance loss and the performance recovery of the WPGS are evaluated. Finally, a new method for judging the loss importance measure of node, the recovery importance measure of node, and the resilience importance measure are proposed. The objective of this research is to analyze the restoration sequence of failed nodes in the WPGS with multi-node failures such that the system recover can be faster and more effective. The proposed method is proven to be effective by introducing the WPGS into the IEEE five-machine 14-node system.

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“…Ghannad et al (2020) proposed a multiobjective optimization-based postdisaster reconstruction scheduling model that considers socioeconomic factors and technical constraints in order to support resource allocation over a postdisaster reconstruction project portfolio. Balakrishnan and Zhang (2020) prioritized vulnerable components of an infrastructure network to support decision making in resilience management through the application of two resilience indexes: a node criticality index, and a node susceptibility index. To be more specific, these two indexes can identify which components in an infrastructure network are vulnerable to disruption in advance and thus enable taking immediate restoration actions in the aftermath of a disaster.…”

mentioning

confidence: 99%

Management of Resilience in Civil Infrastructure Systems: An Interdisciplinary Approach

Naderpajouh

Choi

et al. 2021

J. Manage. Eng.

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Criticality and Susceptibility Indexes for Resilience-Based Ranking and Prioritization of Components in Interdependent Infrastructure Networks

Cited by 19 publications

References 52 publications

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Importance measure-based resilience analysis of a wind power generation system

Management of Resilience in Civil Infrastructure Systems: An Interdisciplinary Approach

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