Neural-network-based models for generating artificial earthquakes and response spectra
The purpose of this paper is to develop efficient neural-network-based models for the generation of artificial earthquake and response spectra. Five neural-network-based models are proposed for replacing traditional processes. Artificial-neural-network-based model-I (ANN-I) substitutes the parameter identification process of empirical model for generating Fourier amplitude spectrum. ANN-II and ANN-III are trained to obtain the parameters of the power spectral density function and the intensity function, respectively. ANN-IV directly generates an acceleration response spectrum with basic information such as magnitude, epicentral distance, site conditions and focal depth. Finally, ANN-V inverses the ANN-IV, so that it can be applied to the generation of synthetic ground motion accelerograms compatible with a target response spectrum. This study shows that the procedure using neural-network-based models is applicable to generate artificial earthquakes and response spectra. Several numerical examples are given to verify the developed models.
artificial earthquake response spectra parameter identification empirical model neural network