Predicting the settlement of shallow foundations on cohesionless soils using back-propagation neural networks

Abstract

Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict the settlement of shallow foundations on cohesionless soils. More than two hundred cases of actual measured settlements are used to develop and verify the ANN model. The predicted settlements found by utilising ANNs are compared with the values predicted by three commonly used deterministic methods. The results indicate that artificial neural networks are a promising method for predicting settlement of shallow foundations on cohesionless soils, as they outperform the conventional methods.