Resilience Assessment under Imprecise Probability

Wang, Cao, Beer, Michael ORCID: 0000-0002-0611-0345, Faes, Matthias GR and Feng, De-Cheng (2024) Resilience Assessment under Imprecise Probability. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10 (2). 04024025-.

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Abstract

Resilience analysis of civil structures and infrastructure systems is a powerful approach to quantifying an object's ability to prepare for, recover from, and adapt to disruptive events. The resilience is typically measured probabilistically by the integration of the time-variant performance function, which is by nature a stochastic process as it is affected by many uncertain factors such as hazard occurrences and posthazard recoveries. Resilience evaluation could be challenging in many cases with imprecise probability information on the time-variant performance function. In this paper, a novel method for the assessment of imprecise resilience is presented, which deals with resilience problems with nonprobabilistic performance function. The proposed method, producing lower and upper bounds for imprecise resilience, has benefited from that for imprecise reliability as documented in the literature, motivated by the similarity between reliability and resilience. Two types of stochastic processes, namely log-Gamma and lognormal processes, are employed to model the performance function, with which the explicit form of resilience is derived. Moreover, for a planning horizon within which the hazards may occur for multiple times, the incompletely informed performance function results in "time-dependent imprecise resilience,"which is dependent on the duration of the service period (e.g., life cycle) and can also be handled by applying the proposed method. Through examining the time-dependent resilience of a strip foundation in a coastal area subjected to groundwater intrusion in a changing climate, the applicability of the proposed resilience bounding method is demonstrated. The impact of imprecise probability information on resilience is quantified through sensitivity analysis.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	09 Apr 2024 07:24
Last Modified:	30 Apr 2024 09:38
DOI:	10.1061/ajrua6.rueng-1244
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180147