用自动化机器学习预测土壤最佳含水率与最大干密度，提升施工效率。
Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach
- 通过AutoML自动选择算法与调参，优化预测模型。
- XGBoost模型在独立数据集上对MDD和OMC的R²分别达80.4%和89.1%。
- 适用于多种土质，适合工程地质与智能施工领域参考。
土壤密实度在道路路堤和土坝等结构稳定性中至关重要。传统确定最优含水率（OMC）和最大干密度（MDD）的方法依赖耗时的实验室试验，经验回归模型在不同土质间适用性与精度有限。近年来，人工智能与机器学习技术成为预测替代方案，但模型常面临准确率低与泛化能力差的问题，尤其在异质数据集上表现不佳。本研究提出一种自动化机器学习（AutoML）方法预测OMC与MDD，其通过自动选择算法与超参数优化，提升模型准确性与可扩展性。实验表明，极端梯度提升（XGBoost）表现最佳，在独立数据集上对MDD的决定系数R²为80.4%，对OMC为89.1%。结果验证了AutoML在多类型土壤中预测密实参数的有效性。研究还强调异质数据集对提升模型泛化性能的重要性。该工作有助于推动更高效可靠的工程建设实践。
原文摘要 · Abstract (English)
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.
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