用可解释机器学习预测砂土中单桩p-y曲线，精度高且原理清晰。
An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
- 基于XGBoost构建可解释的预测模型，输入为桩土参数
- 模型预测精度优于传统方法，验证集表现优异
- 通过SHAP分析揭示关键影响因素，适合工程决策参考
由于桩-土相互作用的复杂性，预测桩体侧向响应极具挑战。机器学习技术因其在非线性分析与预测中的有效性而受到广泛关注。本研究构建了一个可解释的机器学习模型，用于预测砂土中单桩基础的p-y曲线。模型基于现有研究数据集训练，采用XGBoost算法，结果表明其预测精度显著提升。同时，利用Shapley Additive Explanations（SHAP）方法增强模型可解释性。各变量的SHAP值分布与现有理论中影响桩侧向响应的关键因素高度一致，验证了模型的物理合理性。
原文摘要 · Abstract (English)
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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