Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach. (January 2018)
- Record Type:
- Journal Article
- Title:
- Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach. (January 2018)
- Main Title:
- Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach
- Authors:
- Khakzad, Nima
Van Gelder, Pieter - Abstract:
- Highlights: A methodology has been developed for natech risk assessment of industrial plants. Flotation, shell buckling, and rigid sliding are considered as prevailing failure modes. Physical reliability models and Monte Carlo simulation are used to generate artificial failure data. Bayesian parameter learning is used to estimate and combine failure probabilities. Abstract: In the context of natural-technological (natech) accidents, flood-induced damage of industrial plants have received relatively less attention mainly due to the scarcity of such accidents compared to those triggered by earthquakes, high winds, and lightnings. The large amount of oil and chemicals spillage due to floods triggered by the Hurricanes Katrina and Rita in 2005 and Harvey in 2017 in the U.S. demonstrated the potential of floods in causing catastrophic natechs. In the present study, we have developed a methodology based on physical reliability models and Bayesian network so as to assess the fragility (probability of failure) of industrial plants to floods. The application of the methodology has been demonstrated for petroleum storage tanks where flotation, shell buckling, and sliding are considered as the prevailing failure modes. Due to scarcity of empirical data and high-resolution field observations prevailing in natechs, the developed methodology can effectively be applied to a wide variety of natechs in industrial plants as long as limit state equations of respective failure modes canHighlights: A methodology has been developed for natech risk assessment of industrial plants. Flotation, shell buckling, and rigid sliding are considered as prevailing failure modes. Physical reliability models and Monte Carlo simulation are used to generate artificial failure data. Bayesian parameter learning is used to estimate and combine failure probabilities. Abstract: In the context of natural-technological (natech) accidents, flood-induced damage of industrial plants have received relatively less attention mainly due to the scarcity of such accidents compared to those triggered by earthquakes, high winds, and lightnings. The large amount of oil and chemicals spillage due to floods triggered by the Hurricanes Katrina and Rita in 2005 and Harvey in 2017 in the U.S. demonstrated the potential of floods in causing catastrophic natechs. In the present study, we have developed a methodology based on physical reliability models and Bayesian network so as to assess the fragility (probability of failure) of industrial plants to floods. The application of the methodology has been demonstrated for petroleum storage tanks where flotation, shell buckling, and sliding are considered as the prevailing failure modes. Due to scarcity of empirical data and high-resolution field observations prevailing in natechs, the developed methodology can effectively be applied to a wide variety of natechs in industrial plants as long as limit state equations of respective failure modes can reasonably be developed. … (more)
- Is Part Of:
- Reliability engineering & system safety. Volume 169(2018)
- Journal:
- Reliability engineering & system safety
- Issue:
- Volume 169(2018)
- Issue Display:
- Volume 169, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 169
- Issue:
- 2018
- Issue Sort Value:
- 2018-0169-2018-0000
- Page Start:
- 403
- Page End:
- 411
- Publication Date:
- 2018-01
- Subjects:
- Floods -- Natech accidents -- Petroleum storage tank -- Physical reliability models -- Bayesian network
Reliability (Engineering) -- Periodicals
System safety -- Periodicals
Industrial safety -- Periodicals
Fiabilité -- Périodiques
Sécurité des systèmes -- Périodiques
Sécurité du travail -- Périodiques
620.00452 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09518320 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ress.2017.09.016 ↗
- Languages:
- English
- ISSNs:
- 0951-8320
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7356.422700
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 5296.xml