ABSTRACT
This paper presents the application of Artificial Neural Networks (ANNs) to the impedance functions of inclined pile groups. Firstly, the dataset required for the training and testing of the ANNs is obtained through a numerical FE model. A parametric investigation is carried out for the frequency-dependent impedance functions of 2 × 2 pile groups rigidly connected at the head and embedded in homogeneous soil deposits; soil-foundation systems with different geometric characteristic and properties are investigated (e.g. inclination angles, pile-soil stiffness ratios, pile length-to-diameter ratios). subsequently, several ANNs consisting of two hidden layers are trained, tested, and validated to optimise the generalisation performance of the model. Results show that ANNs are able to capture the trends of the impedance functions of inclined pile groups on the basis of the adopted dimensionless parameters. In addition, the complexity of the ANN models achieving good performance confirms the need for advanced regression models.
