A hybrid neural network method for simulating spatial variation in earthquake ground motion

Different excitations for supports should be considered for the analysis of long-span structures. The excitation of each support has time delay and spatial variation relative to other support excitations. The present study aims to propose a new method for simulating accelerograms for various distances considering spatial variation of earthquake records. The accelerograms are simulated based on response or design spectra using the learning capabilities of neural networks. In this method, the response spectrum, and the distance parameter (distance from fault rupture) are the input, and the corresponding accelerograms are the output of the network. There are three stages involved in this study. In the first stage, a replicator neural network is used as a data compressor to increase capability of the simulation. In the second stage, a radial basis function neural network is employed to generate a compressed accelerogram for a certain distance and a response spectrum. In the third stage, the compressed acceleration data is decompressed to resemble real earthquake records. Recorded accelerograms of the strong motion array in Taiwan are used to train the artificial neural network. The obtained results show the robustness of the applied method in producing spatially varying accelerograms. Finally, compatible accelerograms of the design spectrum, suggested in the Taiwan building seismic design code, are simulated for different distances with the proposed method.