Multifactorial Mechanisms and SPSS Multiple Linear Regression-Based Prediction Modeling of Atterberg Limits for Lignosulfonate-Stabilized Red Clay

Abstract

Red clay, inherently high in liquid limit and plasticity, is unsuitable for direct subgrade construction, making green soil stabilization a critical demand for sustainable transportation infrastructure. Lignosulfonate (LS), a by-product of the papermaking industry, exhibits broad prospects for red clay stabilization. This study investigated Fujian high liquid limit red clay, conducting Atterberg limit tests under varying LS contents, mixing methods, and curing ages, combined with a series of micro-tests. SPSS multiple linear regression was employed to quantify factor influence, and high-precision prediction models were established via nonlinear fitting. Results showed that ≥ 1% LS removed soil from the high liquid limit category; optimal content was 3% for calcium lignosulfonate (CLS), 3% for sodium lignosulfonate (SLS) plasticity improvement, and 7% for SLS liquid limit reduction. No new chemical bonds are formed and no significant mineral dissolution occurs during CLS stabilization; plasticity improves via Ca2+ cation exchange and sulfonate-hydroxyl hydrogen bonding, which compresses the electric double layer and forms dense aggregates. All variables had a variance inflation factor (VIF) of 1.000, with LS content as the absolute dominant factor. The models achieved a maximum R2 of 0.997, providing a potential reference for the quantitative design of LS-stabilized red clay subgrades.