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 XGBPO ensemble, achieved superior predictive accuracy with a coefficient of determination (R2) of 0.9914 in the testing phase, outperforming individual and hybrid models. Interpretability and sensitivity analyses further identified dry density (γ d), void ratio (e0), and degree of saturation (Sᵣ) as the most influential factors governing soil compressibility behavior. The proposed framework provides a scalable, reliable, and computationally efficient alternative to traditional geotechnical testing methods, offering improved predictive accuracy and practical applicability for infrastructure design and decision-making under complex soil conditions.