2007 Johansen and Tien 2018 Wu and Dueñas-Osorio 2013), and transportation networks (Hwang et al. 1998), gas (Adachi and Ellingwood 2009 Dueñas-Osorio et al. 2018), water (Chang and Shinozuka 2004), power (Shinozuka et al. So far, the community module's physical system includes data on the community's building inventory (Roohi et al. 2021 Wu and Dueñas-Osorio 2013 Zhang et al. 2000 Johansen and Tien 2018 Lin and El-Tawil 2020 Roohi et al. Since then, the testbed's hazard module has been developed slightly by adding more earthquake scenarios, whereas its community module has been developed significantly by multiple research groups for different research purposes (Adachi and Ellingwood 2009 Chang and Shinozuka 2004 Dueñas-Osorio et al. (1998) initiated the Shelby County, Tennessee testbed in 1998 for investigating the effects of seismic damages to the electrical power system on the local community's economy. This article also provides an overview of internal and external tools suitable for strengthening resilience and presents a possible procedure for their application to energy critical infrastructure elements. For this reason, the aim of this article is to provide the reader with a comprehensive methodological overview of resilience strengthening in the critical energy infrastructure sector. Despite the great importance of this area, there is not a large number of authors moving in this direction and paying attention to resilience-strengthening tools.
However, the issue of strengthening resilience poses a significant challenge not only in the energy sector but also in the entire critical infrastructure system. The resilience assessment of critical infrastructure, especially in the energy sector, has received considerable attention due to the high level of interest in this issue. Thus, the issue of resilience, or its assessment and strengthening, is increasingly coming to the fore. These models enable us to probabilistically infer which interdependencies have the most critical effects and prioritize components for repair or reinforcement to increase resilience.Īs the number of threats and the severity of their impact increases, an ever greater emphasis is being placed on the protection of critical infrastructure. Generalized expressions to create the multi-scale Bayesian network model accounting for each interdependency type are presented and applied to a real interdependent water, power, and gas network to demonstrate their use.
By understanding how these interdependencies affect the fragility of overall systems, infrastructure owners can work towards creating more resilient infrastructure systems that sustain less damage from natural hazards and targeted attacks, and restore services to communities rapidly. We propose a methodology to model interdependencies probabilistically using a novel Bayesian network approach. To understand the ways infrastructure systems depend on one another, we define three comprehensive interdependency types – service provision, geographic, and access for repair. The prevalence of aging infrastructure and an increase in cascading failures have highlighted the need to focus on building strong, interdependent infrastructure systems to increase resilience.
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