An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917890977955840 |
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| author | Li, Biao Song, Qing-Kai Qi, Wen-Gang Gao, Fu-Ping |
| author_facet | Li, Biao Song, Qing-Kai Qi, Wen-Gang Gao, Fu-Ping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand Li, Biao Song, Qing-Kai Qi, Wen-Gang Gao, Fu-Ping Machine Learning Soft Condensed Matter 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. |
| title | An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand |
| topic | Machine Learning Soft Condensed Matter |
| url | https://arxiv.org/abs/2501.06232 |