Prediction of settlement of shallow foundations on reinforced soils using neural networks

The use of reinforcement to increase the bearing capacity and reduce the settlement of shallow foundations is a common construction technique. Although foundation settlement is a major problem for design, few practical methods have been developed to compute the settlement of shallow foundations on reinforced cohesionless soils. In this study, a feedforward backpropagation neural network (BPNN), which is one type of artificial neural network ( ANN), is used to predict the settlement of reinforced foundations. The model performance showed very good agreement with the measured settlements. The results indicate that the developed BPNN model may be a powerful tool to accurately predict settlement of shallow foundations on reinforced cohesionless soils.