ADVANCES IN MODELLING WAVE-STRUCTURE INTERACTION THROUGH ARTIFICIAL NEURAL NETWORKS

Abstract

This contribution describes a new Artificial Neural Network (ANN) able to predict at once the main parameters representative of the wave-structure interaction processes, i.e. the wave overtopping discharge, the wave transmission coefficient and the wave reflection coefficient. The development of this ANN started with the preparation of a new extended and homogeneous database (derived from CLASH database) which collects all the available tests including at least one of the three parameters, for a total amount of 16,165 data. Some of the existing ANNs were compared and improved, leading to the selection of a final ANN, whose architecture was optimized through an in-depth sensitivity analysis to the learning and training ANN features. The new ANN here proposed provides accurate predictions for all the three parameters, resulting in a tool that can be efficiently used for design purposes.