arXiv Open Access 2025

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

Biao Li Qing-Kai Song Wen-Gang Qi Fu-Ping Gao

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.LG cond-mat.soft

Penulis (4)

Biao Li

Qing-Kai Song

Wen-Gang Qi

Fu-Ping Gao

Format Sitasi

APA MLA BibTeX

Li, B., Song, Q., Qi, W., Gao, F. (2025). An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand. https://arxiv.org/abs/2501.06232

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓