Application of Physics-Informed Neural Networks for forward and inverse analysis of pile-soil interaction

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) modeling 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.

Automated summary

This paper presents the application of Physics-Informed Neural Networks (PINNs) in the forward and inverse analysis of pile-soil interaction problems. The main challenge in the Artificial Neural Network (ANN) modeling of such interaction is the abrupt changes in material properties that cause discontinuities in displacement solution gradient. To address this, a domain-decomposition multi-network model is proposed to handle the strain field discontinuities at common boundaries of pile-soil regions and soil layers. The model is demonstrated on the analysis and parametric study of single piles embedded in homogeneous and layered formations, under axisymmetric and plane strain conditions. The performance of the model in inverse analysis of pile-soil interaction is particularly investigated, showing successful inversion of soil parameters in layered formations using localized data obtained via fiber optic strain sensing along the pile length.