Technical Papers
Sep 15, 2023

Optimal Bridge Retrofitting Selection for Seismic Risk Management Using Genetic Algorithms and Neural Network–Based Surrogate Models

Publication: Journal of Infrastructure Systems
Volume 29, Issue 4

Abstract

This study used genetic algorithms as part of an optimization framework to directly minimize the expected impacts of road network disruption triggered by seismic events. This minimization is achieved by selecting an optimal set of bridges to retrofit to decrease their probability of being unavailable after an earthquake. We propose a genetic algorithm that outperforms other retrofitting techniques, such as ranking bridges by vulnerability or traffic importance. The proposed framework was demonstrated using the San Francisco road network as a testbed. This example showed that bridges selected by genetic algorithms are structurally vulnerable groups of bridges that act as corridors in the network. Additionally, this study evaluated and recommends domain reduction techniques and hyperparameter calibrations that can decrease the computational costs of this approach.

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Data Availability Statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request, including the code used for the implementation of genetic algorithms and the surrogate model for the road network.

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Go to Journal of Infrastructure Systems
Journal of Infrastructure Systems
Volume 29Issue 4December 2023

History

Received: Oct 3, 2022
Accepted: Apr 7, 2023
Published online: Sep 15, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 15, 2024

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Rodrigo Silva-Lopez, Ph.D., S.M.ASCE https://orcid.org/0000-0001-9280-461X [email protected]
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Stanford Univ., Stanford, CA 94305 (corresponding author). ORCID: https://orcid.org/0000-0001-9280-461X. Email: [email protected]
Jack W. Baker, M.ASCE
Professor, Dept. of Civil and Environmental Engineering, Stanford Univ., Stanford, CA 94305.

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