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Title: Path-Dependent Reliability and Resiliency of Critical Infrastructure via Particle Integration Methods
Critical infrastructure is the backbone of modern societies. To meet increasing demand under resource-constrained and multihazard conditions, policy-makers are tapping into infrastructure resiliency: its capacity to withstand and recover from disruptions. Thus, resiliency-aware uncertainty quantification is key to identify tipping points, yet it remains computationally inaccessible. This paper maps resiliency measures to well understood time-dependent reliability computations, porting insights and methods from reliability theory to the service of critical infrastructure resiliency and upkeep efforts. For large-scale applications, we use particle integration methods (PIMs)—a family of sequential Monte Carlo methods with wide-ranging applications—and propose their optimal tuning in terms of their variance and number of limit-state function evaluations. We obtain consistent and unbiased probability estimates in applications to dynamical systems, network reliability, and resilience analysis, demonstrating PIMs as practical yet under-appreciated tools. For example, we obtain probability estimates of order 10−14 in networks with over 10,000 random variables.  more » « less
Award ID(s):
2037545
PAR ID:
10410338
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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