Optimizing XGBoost, CatBoost, and Bagging models for predicting the maximum dry density of compacted soil using grid search hyperparameter tuning - Thai Pham, Binh; Prakash, Indra (Vietnam Journal of Earth Sciences, 2025)
In geotechnical engineering, an accurate estimation of maximum dry density (MDD) is essential to ensure the stability of geotechnical structures such as roads, embankments, and foundations. While traditional laboratory methods, such as the Proctor compaction test, are reliable, they are often labor-intensive and time-consuming. Therefore, the main aim of this study is to develop efficient data-driven models, including XGBoost (XGB), CatBoost (CAB), and Bagging (BAG), for rapid and reliable estimation of MDD using easily measurable soil properties. A dataset of 214 soil samples collected from the Van Don-Mong Cai expressway construction project (Vietnam) comprising eight key input variables was used: gravel content, coarse and fine sand contents, silt and clay content, optimum moisture content, liquid limit, plastic limit, and plasticity index. Model performance was evaluated using R², RMSE, MAE, and a Taylor diagram. Results indicate that the Grid Search-optimized BAG model achieved the best performance, with R² values of 0.94 and 0.81 for the training and test datasets, respectively, and the lowest RMSE and MAE. Optimized CAB showed comparable performance, while XGB exhibited relatively lower generalization capability. Optimized CAB yielded similar results, whereas optimized XGB performed worse. The significance of this study lies in demonstrating that ensemble learning models, particularly Bagging, can provide accurate, physically interpretable predictions of MDD, thereby reducing reliance on extensive laboratory testing. The novelty of this work lies in the systematic comparison of optimized ensemble models using a real construction dataset, combined with interpretability analysis via partial dependence plots consistent with established soil mechanics principles. These findings highlight the potential of optimized machine learning models as practical tools for modern geotechnical engineering applications.