  The application of the Physics-Informed Neural Networks (PINNs) to forward
and inverse analysis of pile-soil interaction problems is presented. The main
challenge encountered in the Artificial Neural Network (ANN) modelling of
pile-soil interaction is the presence of abrupt changes in material properties,
which results in large discontinuities in the gradient of the displacement
solution. Therefore, a domain-decomposition multi-network model is proposed to
deal with the discontinuities in the strain fields at common boundaries of
pile-soil regions and soil layers. The application of the model to the analysis
and parametric study of single piles embedded in both homogeneous and layered
formations is demonstrated under axisymmetric and plane strain conditions. The
performance of the model in parameter identification (inverse analysis) of
pile-soil interaction is particularly investigated. It is shown that by using
PINNs, the localized data acquired along the pile length - possibly obtained
via fiber optic strain sensing - can be successfully used for the inversion of
soil parameters in layered formations.
