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

ABSTRACT: 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 effectivel

SUBMITTER: Zhou W 

PROVIDER: S-EPMC12788692 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature