一种用于预测砂土中单桩基础p-y曲线的可解释机器学习模型
An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
AI总结：
本研究针对砂土中单桩基础p-y曲线预测难题，构建基于XGBoost的可解释机器学习模型，用现有研究数据库训练获高预测精度，并用SHAP验证变量影响符合既有理论。
摘要：
由于桩-土相互作用的复杂性，预测横向桩响应颇具挑战。机器学习（ML）技术凭借其在非线性分析与预测方面的有效性已受到广泛关注。本研究构建了一种可解释的基于机器学习的模型，用于预测单桩基础的p-y曲线。研究利用从现有研究中整理的数据库训练了一个XGBoost模型，结果表明该模型实现了优异的预测精度。研究采用Shapley Additive Explanations（SHAP，沙普利可加解释）来提升可解释性，各变量的SHAP值分布与桩基础横向响应影响因素的既有理论知识高度吻合。
英文摘要：
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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