Deep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks

Abstract

Seismic risk assessment of road systems involves computationally expensive traffic simulations to evaluate the performance of the system. To accelerate this process, this paper develops a neural network surrogate model that allows rapid and accurate estimation of changes in traffic performance metrics due to bridge damage. Some of the methodological aspects explored when calibrating this neural network are defining sampling protocols, selecting hyperparameters, and evaluating practical considerations of the model. In addition to the neural network, a modified version of the local interpretable model-agnostic explanation (LIME) is proposed as a retrofitting strategy that minimizes earthquakes’ impact on the system. The modified version (LIME-TI) uses traffic impacts (TI) and rates of occurrence to aggregate the importance of individual damage realizations during the computation of variable importance. This study uses the San Francisco Bay Area road network as a testbed. As a conclusion of this study, the neural network accurately predicts the system’s performance while taking five orders of magnitude less time to compute traffic metrics, allowing decision-makers to evaluate the impact of retrofitting bridges in the system quickly. Moreover, the proposed LIME-TI metric is superior to others (such as traffic volume or vulnerability) in identifying bridges whose retrofit effectively improves network performance.