Seismic isolated bridge performance evaluation and design proposal using artificial neural network

Abstract

AbstractSeismic isolators such as rubber bearings and other dampers are critical components of seismic design. The hysteresis behavior of such bearings is complicated, and engineers are using a bilinear model or equivalent linearization method in design practice. The response evaluation and initial design parameter assumptions are highly dependent on engineers' expertise and need repeated analysis. Therefore, this study proposed a machine learning‐based approach to conduct bridge seismic isolation performance evaluation and design parameters proposal without repeating the nonlinear simulations. The training data were generated by conducting a nonlinear time history analysis of the bridge structure model with input design earthquakes. Four trained three‐layer neural network models could perform bridge seismic response evaluation, predict the structure's seismic response, and suggest design proposal using some input parameters.