Construction of multi-parameter intelligent comprehensive estimation algorithms for soil compression modulus.

Accurate prediction of soil stress-strain behavior remains a major challenge in geotechnical engineering due to the inherent heterogeneity, nonlinearity, and sparsity of soil datasets. Conventional laboratory and in-situ testing methods are often expensive, time-consuming, and sensitive to sampling disturbances, which limits their efficiency in large-scale engineering applications. To address these challenges, this study proposes an optimized stacking ensemble framework that integrates advanced tree-based learning algorithms with metaheuristic optimization. The selected base learners Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGB), Random Forest (RFR), and Histogram-based Gradient Boosting (HGB) were chosen for their complementary strengths in capturing nonlinear interactions, handling high-dimensional inputs, and maintaining robustness under sparse and heterogeneous data conditions. These models are optimized using the Puma Optimization (PO) algorithm and combined through a stacking strategy to enhance predictive stability and generalization performance. A dataset comprising 1,410 samples was compiled literature data, witch the K-fold cross-validation was employed to evaluate model robustness. The proposed stacking model, particularly the optimized XGB