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.