Modeling ground motion in Mexico City using artificial neural networks

Abstract

After the September 1985 earthquakes in Mexico City, many strong motion instruments were laid down throughout the Valley of Mexico. Since then, a wealth of valuable information has been gathered. This has provided an excellent opportunity to develop new analytical procedures based on knowledge-based techniques.
An Artificial Neural Network (ANN) is a computational mechanism able to acquire, represent, and compute a mapping from one multivariate space of information to another, given a set of data representing that mapping. Accordingly, research aimed at developing an ANN to model the earthquake response of Mexico City soil deposits was initiated a few years ago. The resulting network that allows the computation of the response of the clayey ground is presented and discussed in this paper. It is shown that well designed networks represent a genuine alternative to analytical methods.