Influence mechanism exploration and machine learning prediction of loess compression deformation coefficient under multi-factor coupling effects.

Accurate prediction of the compression deformation coefficient of loess fillers is key for stability assessment of loess subgrade engineering. In this study, the effects of key influencing factors such as compaction, water content, vertical pressure and molding method on compression characteristics were investigated. Four machine learning models, XGBoost (XGB), Support Vector Regression (SVR), Backpropagation Neural Network (BP), and Sparrow Search Algorithm-optimized BP (SSA-BP), were developed to predict the deformation coefficient using experimental datasets. SHAP interpretability analysis quantified feature contributions and coupling effects. Results demonstrate that: Vibration compaction, increased compaction, and reduced water content enhance particle interlocking, thereby effectively suppressing deformation and reducing the compression deformation coefficient; The coefficient significantly increases with rising vertical pressure in compacted loess. The metaheuristic-optimized SSA-BP model demonstrated superior performance with a test set RMSE of 0.138%, significantly outperforming both the SVR、XGB and standard BP models by 35%、45%and 46%, respectively. SHAP analysis revealed vertical pressure as the most influential factor and identified significant nonlinear interactions, particularly between vertical pressure and water content. These findings provide both a reliable prediction tool and mechanistic insights for loess subgrade engineering.