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

Abstract

This study uses 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 outstrips other retrofitting techniques, such as ranking bridges by vulnerability or traffic importance. The proposed framework is demonstrated using the San Francisco Road Network as a testbed. This example shows that bridges selected by genetic algorithms are structurally vulnerable groups of bridges that act as corridors in the network. Additionally, this study evaluates and recommends domain reduction techniques and hyperparameter calibrations that can decrease the computational costs of this approach.