Predicting settlement of shallow foundations using neural networks

Over the years, many methods have been developed to predict the settlement of shallow foundations on cohesionless soils. However, methods for making such predictions with the required degree of accuracy and consistency have not yet been developed. Accurate prediction of settlement is essential since settlement, rather than bearing capacity, generally controls foundation design. In this paper, artificial neural networks (ANNs) are used in an attempt to obtain more accurate settlement prediction. A large database of actual measured settlements is used to develop and verify the ANN model. The predicted settlements found by utilizing ANNs are compared with the values predicted by three of the most commonly used traditional methods. The results indicate that ANNs are a useful technique for predicting the settlement of shallow foundations on cohesionless soils, as they outperform the traditional methods.