An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand

• URL: http://arxiv.org/abs/2501.06232v1
• Date: Wed, 08 Jan 2025 03:00:34 GMT
• Title: An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
• Authors: Biao Li, Qing-Kai Song, Wen-Gang Qi, Fu-Ping Gao,
• Abstract summary: This study develops an interpretable machine learning-based model for predicting p-y curves of monopile foundations. The results demonstrate that the model achieves superior predictive accuracy.
• Score: 5.0649910056131775
• License:
• Abstract: 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.

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