An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
This work addresses the problem of predicting p-y curves for monopile foundations in sand, which is important for geotechnical engineers, but it is incremental as it applies an existing ML method to a specific domain.
The study tackled the challenge of predicting lateral pile response in monopile foundations by developing an interpretable machine learning model using XGBoost, which achieved superior predictive accuracy and used SHAP to align variable importance with theoretical knowledge.
Predicting the lateral pile response is challenging due to the complexity of pile-soil interactions. Machine learning (ML) techniques have gained considerable attention for their effectiveness in non-linear analysis and prediction. This study develops an interpretable ML-based model for predicting p-y curves of monopile foundations. An XGBoost model was trained using a database compiled from existing research. The results demonstrate that the model achieves superior predictive accuracy. Shapley Additive Explanations (SHAP) was employed to enhance interpretability. The SHAP value distributions for each variable demonstrate strong alignment with established theoretical knowledge on factors affecting the lateral response of pile foundations.