Broadband ground motions from 3D physics-based numerical simulations using artificial neural networks

In this article, a novel strategy to generate broadband earthquake ground motions from the results of 3D physics-based numerical simulations (PBSs) is presented. Physics-based simulated ground motions embody a rigorous seismic-wave propagation model (i.e., including source, path, and site effects), which is however reliable only in the long-period range (typically above 0.75–1 s), owing to the limitations posed both by computational constraints and by insufficient knowledge of the medium at short wavelengths. To cope with these limitations, the proposed approach makes use of Artificial Neural Networks (ANNs), trained on a set of strong-motion records, to predict the response spectral ordinates at short periods. The essence of the procedure is, first, to use the trained ANN to estimate the short-period response spectral ordinates using as input the long-period ones obtained by the PBS, and, then, to enrich the PBS time histories at short periods by scaling iteratively their Fourier spectrum, with no phase change, until their response spectrum matches the ANN target spectrum. After several validation checks of the accuracy of the ANN predictions, the case study of the 29 May 2012 Mw 6.0 Po Plain earthquake is illustrated as a comprehensive example of application of the proposed procedure. The capability of the proposed approach to reproduce in a realistic way the engineering features of earthquake ground motion, including the peak values and their spatial correlation structure, is successfully proven