Artificial neural network−based simulated site amplification models for Central and Eastern North America

This article explores the use of deep learning/artificial neural network (ANN)-based response spectrum (RS) and Fourier amplitude spectrum (FAS) site amplification models along with their standard deviations for simulated one-dimensional (1D) site response in Central and Eastern North America (CENA) using over 3.6 million 1D site response simulations. ANNs are demonstrated to significantly decrease the bias in the estimations (e.g. standard deviation of models’ residuals) and to better capture the features of site-specific amplification (e.g. the attributes of peak amplification) as compared to their conventional statistical regression counterparts that were derived from the same simulated amplification data. This improved performance includes site responses at shallow sites, which have been a challenge to effectively model previously. Multiple ANN models, each with different input variables but using the same ANN structure, are explored to represent diverse simulated amplification datasets (e.g. linear vs nonlinear or RS vs FAS), demonstrating beneficial features of ANN approach. In addition, ANN-based models are found to be useful in identifying controlling parameters and the minimum levels of site-to-site variability that are achievable given the conditioning variables.