| Title |
The Generation of Artificial Earthquakes and Response Spectra using Neural-Network-based Models |
| Keywords |
Neural Network ; Artificial Earthquake ; Response Spectra ; Empirical Model ; Parameter Identification |
| Abstract |
The purpose of this paper is to develop efficient neural-network-based models for the generation of artificial earthquake and response spectra. Seven neural-network-based models are proposed for replacing traditional processes. FAS_NN substitutes the parameter identification process of empirical model for generating Fourier amplitude spectrum (FAS). PDS_NN and INT_NN are trained to obtain the parameters of the power spectral density function (PSD) and the intensity function (INT), respectively. RS_NN, ARS_NN and DS_NN directly generate an acceleration response spectrum (RS) and design spectrum (DS) with basic information such as magnitude, epicentral distance, site conditions and focal depth. Finally, IARS_NN inverses the ARS_NN, 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. |