Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum moisture content (OMC) and maximum dry density (MDD) involve labor-intensive laboratory experiments, and empirical regression models have limited applicability and accuracy across diverse soil types. In recent years, artificial intelligence (AI) and machine learning (ML) techniques have emerged as alternatives for predicting these compaction parameters. However, ML models often struggle with prediction accuracy and generalizability, particularly with heterogeneous datasets representing various soil types. This study proposes an automated machine learning (AutoML) approach to predict OMC and MDD. AutoML automates algorithm selection and hyperparameter optimization, potentially improving accuracy and scalability. Through extensive experimentation, the study found that the Extreme Gradient Boosting (XGBoost) algorithm provided the best performance, achieving R-squared values of 80.4% for MDD and 89.1% for OMC on a separate dataset. These results demonstrate the effectiveness of AutoML in predicting compaction parameters across different soil types. The study also highlights the importance of heterogeneous datasets in improving the generalization and performance of ML models. Ultimately, this research contributes to more efficient and reliable construction practices by enhancing the prediction of soil compaction parameters.

翻译：土壤压实对于建筑工程中确保路堤、土坝等结构的稳定性至关重要。传统确定最优含水率（OMC）与最大干密度（MDD）的方法依赖于劳动密集型的室内试验，而经验回归模型在不同土壤类型中的适用性与准确性有限。近年来，人工智能（AI）与机器学习（ML）技术已成为预测这些压实参数的新兴替代方案。然而，机器学习模型在预测精度与泛化能力方面常面临挑战，尤其是在处理代表多种土壤类型的异质数据集时。本研究提出一种自动化机器学习（AutoML）方法，用于预测OMC与MDD。AutoML通过自动化算法选择与超参数优化，有望提升预测精度与可扩展性。经大量实验验证，研究发现极端梯度提升（XGBoost）算法表现最佳，在独立数据集上对MDD和OMC的预测分别达到了80.4%和89.1%的R平方值。这些结果证明了AutoML在跨不同土壤类型预测压实参数方面的有效性。研究还强调了异质数据集对提升机器学习模型泛化能力与性能的重要性。最终，本研究通过改进土壤压实参数的预测，为更高效、可靠的工程实践提供了支持。