ARTIFICIAL NEURAL NETWORK BASED ESTIMATION OF PEAK GROUND ACCELERATION

Abstract

This paper presents the application of artificial neural networks (ANN) for estimation of peak ground acceleration (PGA) for earthquakes of magnitude more than 5.0 and hypocentral distance of less than 50 km. Earthquake magnitude, hypocentral distance, and average values of four geophysical properties of the site (Standard Penetration Test (SPT) blow count, primary wave velocity, shear wave velocity, and density of soil) have been used as six input variables to train the neural network. An attempt has also been made to train the neural network with magnitude, hypocentral distance and average shear wave velocity as three input variables. This study shows that ANN is a valuable tool for prediction of peak ground acceleration at a site, given the magnitude and location of earthquake and local soil conditions. It has also been observed that the prediction using the trained network with six inputs is better than that of three inputs.