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Probabilistic metric of infrastructure resilience considering time-dependent and time-independent covariates
UiT The Arctic University of Norway, Norway.
UiT The Arctic University of Norway, Norway.
UiT The Arctic University of Norway, Norway.
RISE - Research Institutes of Sweden, Safety and Transport, Safety.ORCID iD: 0000-0002-4551-1045
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2017 (English)In: Safety and Reliability - Theory and Applications - Proceedings of the 27th European Safety and Reliability Conference, ESREL 2017, CRC Press/Balkema , 2017, p. 1053-1060Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, the importance of resilient critical infrastructures has become more evident. More frequent extreme weather conditions and human-induced disasters, such as terror attacks, cause severe damage to infrastructures. It is important to be able to withstand such events, but perhaps even more important be able to bounce back and rapidly recover. In this work, resilience is formulated, in a pragmatic way, as a combination of the reliability of infrastructure elements, vulnerability and the recoverability of the failed components. To be able to characterize the recovery time, there is a need to know the reliability and vulnerability of the infrastructures, i.e. their drop in performance in different scenarios with different stress level. Moreover, recovery time and vulnerability can be affected significantly by different factors such as location, seasonal effects, recovery crew available etc. Hence, the trajectory of the loss in performance and the recovery may have different paths depending these associated factors, meaning that resilience prediction model must be able to capture these factors. However, resilience studies are not well detailed regarding the effect of time dependent and time independent influence factors. The proposed formulation makes it possible to predict the resilience of a (critical) infrastructure with multiple failure mechanisms, different types of vulnerability process, and recovery actions with time-dependent and time-independent covariates. © 2017 Taylor & Francis Group, London.

Place, publisher, year, edition, pages
CRC Press/Balkema , 2017. p. 1053-1060
Keywords [en]
Failure (mechanical), Recovery, Reliability theory, Extreme weather conditions, Infrastructure resiliences, Multiple failure mechanisms, Prediction model, Recoverability, Recovery actions, Seasonal effects, Time independents, Critical infrastructures
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Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:ri:diva-38108DOI: 10.1201/9781315210469-134Scopus ID: 2-s2.0-85061370930ISBN: 9781138629370 (print)OAI: oai:DiVA.org:ri-38108DiVA, id: diva2:1294814
Conference
27th European Safety and Reliability Conference, ESREL 2017, 18 June 2017 through 22 June 2017
Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-06-27Bibliographically approved

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Lange, DavidHonfi, Daniel

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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Output format
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