Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks

Several experimental and theoretical investigations have been carried out to predict the bearing capacity of shallow foundations on reinforced cohesionless soils. It has been demonstrated that placing layers of reinforcement within the foundation soil increases the bearing capacity of shallow foundations remarkably. A limited number of relations has been suggested for predicting the bearing capacity of shallow foundations on reinforced cohesionless soils. In this paper two common types of artificial neural network (ANN), feedforward backpropagation (BP) and radial basis function (RBF), are used to predict the bearing capacity of shallow foundations on reinforced cohesionless soils based on laboratory and field measurements. The results are then compared with the previous traditional methods, showing a much greater degree of accuracy.