Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

ABSTRACT This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density ( M DD ) and optimum moisture content ( O MC ), which are critical for stabilizing construction foundations. Traditional methods for determining M DD and O MC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy ( E ). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for M DD and O MC , outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that M DD and O MC were most influenced by plastic limit (PL), compaction energy ( E ), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between M DD , O MC , and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.