Use of Neural network to predict the peak ground accelerations and pseudo spectral accelerations for Mexican Inslab and Interplate Earthquakes

Abstract

The use of Artificial Neural Networks (ANN) is explored to predict peak ground accelerations (PGA) and pseudospectral acceleration (SA) for Mexican inslab and interplate earthquakes. A total of 277 and 418 seismic records with two horizontal components for inslab and interplate earthquakes, respectively, are used to train the ANN models by using an ANN with a feed-forward architecture with a back-propagation learning algorithm. Both ANN with single and two hidden layers are considered. For comparison purposes, the PGA and SA values predicted by the trained ANN models are compared with those estimated with attenuation relations or ground motion prediction equations (GMPEs). The comparison indicates that the predicted PGA and SA values by the trained ANN models, in general, follow the trends predicted by the GMPEs. However, an extensive verification of the trained models must be conducted before they can be used for seismic hazard and risk analysis since, on occasion, the PGA and SA values predicted by the trained ANN models depart from the behaviour observed from the actual records.