Prediction of pile shaft resistance using cone penetration tests (CPTs)

Accurately predicting pile shaft resistance when designing pile foundations is necessary for ensuring appropriate structural and serviceability performance. The scope of this research includes four main components: (I) compiling shaft resistance datasets obtained from the published literature; (II) developing two artificial neural network (ANN) and non-linear multi regression models for predicting pile shaft resistance using cone penetration test (CPT) results; (III) investigating the influence of input parameters on the resulting shaft friction and their degrees of importance; and (IV) assessing the relative accuracies of the presented models using a number of traditional methods. It is quantitatively demonstrated that the ANN and non-linear multiple regression models proposed in the current study out perform the traditional methods and can be used by engineers to accurately predict pile shaft resistance.