Efficient System Reliability–Based Disaster Resilience Analysis of Structures Using Importance Sampling
Publication: Journal of Engineering Mechanics
Volume 150, Issue 11
Abstract
Disaster resilience is an emerging concept for managing the risk of civil structural systems considering not only structural safety against extreme loads but also aftermath recovery efforts. The system reliability–based resilience analysis framework facilitates the quantitative evaluation of the disaster resilience performance of structural systems by assessing reliability and redundancy for numerous disruption scenarios. However, its practical application is limited due to the substantial number of structural analyses required to estimate the reliability and redundancy indices. To address the computational challenges, this paper proposes a new importance sampling algorithm, termed Importance Sampling for Noteworthy Scenarios (ISNS). The ISNS algorithm leverages a Gaussian process–based principal point search method to identify initial disruption scenarios that dominantly impact the structure’s resilience. Subsequently, a mixture-based distribution is constructed to represent the near-optimal importance sampling density characterizing the failure domains of the noteworthy disruption scenarios. The two-step procedure enables the simultaneous estimation of both reliability and redundancy indices of the identified noteworthy scenarios. Furthermore, an active learning scheme is incorporated to efficiently train surrogates. Numerical examples of engineering applications are investigated to demonstrate the improved efficiency offered by the proposed method. However, the proposed method faces limitations in multihazard contexts and high-dimensional, highly nonlinear scenarios. These limitations necessitate further validation of the Gaussian process and mixture models to ensure robustness.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The first author was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (RS-2024-00407901). The third and fourth authors were supported by the Ajou University research fund. The fourth author was also supported by a National Research Foundation of Korea grant funded by the Korea government (MSIT) (RS-2023-00242859). All of this support is gratefully acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 150 • Issue 11 • November 2024
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© 2024 American Society of Civil Engineers.
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Received: Jan 16, 2024
Accepted: Jun 11, 2024
Published online: Aug 27, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 27, 2025
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