Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression. (April 2020)

• Record Type: Journal Article
• Authors: Yang, Yunfeng; Chen, Guohua; Reniers, Genserik
• Is Part Of: Reliability engineering & system safety. Volume 196(2020)
• Publication Date: 2020-04
• Subjects: Flood disaster -- Vulnerability -- Fragility magic cube -- Fragility curve -- Logistic regression; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2019.106721
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Fragility models that can be used in the extensive tanks and floods are proposed Effects of key parameters on vulnerability are analyzed using fragility curves Fragility magic cubes are first proposed to obtain the critical damage conditions Mitigation measures are proposed based on the results of vulnerability assessment Abstract: Atmospheric storage tanks damaged by floods may lead to severe NaTech accident scenarios, and the vulnerability assessment of process equipment suffered by natural events is a crucial point in NaTech risk analysis. In the present study, limit state equations for failure modes of displacement and buckling are introduced based on load-resistance relationships. The parameterized fragility models that can be used in a wide variety of atmospheric unanchored storage tanks and floods have been developed based on logistic regression (LR), and moreover, the models are assessed and validated by receiver operating characteristic (ROC) curves and available accident data in literature. The effects of filling level and density of storaged liquid, the diameter and height of tank, and the inundation height and velocity of flood on the vulnerability of different failure modes are analyzed using fragility curves. Furthermore, fragility magic cubes are first proposed to obtain critical disaster conditions and critical filling levels in different cases. Finally, corresponding quantitative mitigation measures in different stages such as site selection, design, and operation are proposed based on the results of vulnerability assessment.