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This content will become publicly available on June 27, 2024

Title: Smart resilience: Capturing dynamic, uncertain and evolving lifecycle conditions
Modern cities are becoming increasingly smart and interconnected, with the capacity to gather unprecedented amounts of information. However, available methods for resilience quantification lack agility to cope with the ever-changing conditions and data that underpin disaster resilience and lifecycle performance analysis. In this paper, we discuss the limitations in the models themselves, i.e. even though frameworks predict uncertain and temporally evolving system performance, they are unable to learn from new data. To address these limitations, we pose a ‘smart resilience modeling concept’ which presents the ability to update model estimations and to efficiently estimate the lifecycle resilience as new data emerges. Hypothetical examples on community infrastructure affected by deterioration effects and punctuated events are presented. This conceptualization is expected to lay a foundation for smart resilience models capable of capturing the dynamic, uncertain, and evolving characteristics of future environmental demands, societal characteristics, and infrastructure conditions.  more » « less
Award ID(s):
2227467
NSF-PAR ID:
10491312
Author(s) / Creator(s):
;
Editor(s):
Fabio Biondini, Dan M.
Publisher / Repository:
Taylor & Francis Group
Date Published:
Journal Name:
PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON LIFE-CYCLE CIVIL ENGINEERING (IALCCE 2023)
Edition / Version:
1st
Page Range / eLocation ID:
341 - 348
Format(s):
Medium: X
Location:
London
Sponsoring Org:
National Science Foundation
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