Path-Dependent Reliability and Resiliency of Critical Infrastructure via Particle Integration Methods
| dc.citation.conferenceDate | 2022-09 | en_US |
| dc.citation.conferenceName | The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022) | en_US |
| dc.citation.journalTitle | The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022) | en_US |
| dc.contributor.author | Paredes, Roger | en_US |
| dc.contributor.author | Talebiyan, Hesam | en_US |
| dc.contributor.author | Dueñas-Osorio, Leonardo | en_US |
| dc.date.accessioned | 2022-05-13T16:01:31Z | en_US |
| dc.date.available | 2022-05-13T16:01:31Z | en_US |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | Paredes, Roger, Talebiyan, Hesam and Dueñas-Osorio, Leonardo. "Path-Dependent Reliability and Resiliency of Critical Infrastructure via Particle Integration Methods." <i>The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022),</i> (2022) IASSAR: <a href="https://hdl.handle.net/1911/112396">https://hdl.handle.net/1911/112396</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/112396 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IASSAR | en_US |
| dc.rights | This work is made available with a Creative Commons License - Public Domain (CC0). | en_US |
| dc.rights.uri | https://creativecommons.org/publicdomain/zero/1.0/ | en_US |
| dc.subject.keyword | Dynamic | en_US |
| dc.subject.keyword | Reliability | en_US |
| dc.subject.keyword | Resiliency | en_US |
| dc.subject.keyword | Sequential Monte Carlo | en_US |
| dc.subject.keyword | Subset Simulation | en_US |
| dc.subject.keyword | Uncertainty Quantification | en_US |
| dc.title | Path-Dependent Reliability and Resiliency of Critical Infrastructure via Particle Integration Methods | en_US |
| dc.type | Conference paper | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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