Multilayer Feedforward Neural Network Models for Pattern Recognition Tasks in Earthquake Engineering

Abstract

Neural network models are successfully used in many pattern recognition tasks because of their ability to capture the features and also to capture the nonlinear hypersurfaces dividing the classes in the feature space. Over the last few years or so the use of artificial neural networks (ANNs) has increased in many areas of engineering. In particular, multilayer feedforward neural network (MLFFNN) models have been applied to many geotechnical problems. They have demonstrated some degree of success. MLFFNN models have been used successfully in pile capacity prediction, modeling soil behavior, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. In this paper we propose to use MLFFNN models for the task of earthquake risk evaluation.