Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis

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dc.contributor.authorLiu, Xing
dc.contributor.authorFerrario, Elisa
dc.contributor.authorZio, Enrico
dc.date.accessioned2025-01-23T21:11:58Z
dc.date.available2025-01-23T21:11:58Z
dc.date.issued2019
dc.description.abstractIn interdependent critical infrastructures (ICIs), a disruptive event can affect multiple system elements and system resilience is greatly dependent on uncertain factors, related to system protection and restoration strategies. In this paper, we perform sensitivity analysis (SA) supported by importance measures to identify the most relevant system parameters. Since a large number of simulations is required for accurate SA under different failure scenarios, the computational burden associated with the analysis may be impractical. To tackle this computational issue, we resort to two different approaches. In the first one, we replace the long-running dynamic equations with a fast-running Artificial Neural Network (ANN) regression model, optimally trained to approximate the response of the original system dynamic equations. In the second approach, we apply an ensemble based method that aggregates three alternative SA indicators, which allows reducing the number of simulations required by a SA based on only one indicator. The methods are implemented into a case study consisting of interconnected gas and electric power networks. The effectiveness of these two approaches is compared with those obtained by a given data estimation SA approach. The outcomes of the analysis can provide useful insights to the shareholders and decision-makers on how to improve system resilience.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.ress.2019.04.017
dc.identifier.eissn1879-0836
dc.identifier.issn0951-8320
dc.identifier.urihttps://doi.org/10.1016/j.ress.2019.04.017
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/100933
dc.identifier.wosidWOS:000474493000035
dc.language.isoen
dc.pagina.final434
dc.pagina.inicio423
dc.revistaReliability engineering & system safety
dc.rightsacceso restringido
dc.subjectCritical infrastructure
dc.subjectSystem resilience
dc.subjectImportance measure
dc.subjectSensitivity analysis
dc.subjectArtificial neural networks
dc.subjectEnsemble of methods
dc.titleIdentifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis
dc.typeartículo
dc.volumen189
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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