Research Information System
Asian Journal of Civil Engineering, 2026 (Scopus)
Accurate prediction of the unconfined compressive strength (UCS) of stabilized expansive soils is vital for reliable geotechnical design. This study proposes a Slime Mould Algorithm (SMA), an optimized machine learning framework to predict the UCS of expansive soils treated with hydrated-lime-activated rice husk ash (HARHA). A database of 121 laboratory-tested samples was developed using seven input parameters: HARHA content, liquid limit, plastic limit, plasticity index, optimum moisture content, clay activity, and maximum dry density. Several machine learning models were evaluated in both conventional and SMA-optimized forms using R2, RMSE, MAE, and MSE. SMA optimization significantly improved model performance, increasing R2 by 3–7% and reducing RMSE by up to 25%. The optimized models achieved R2 values above 0.98, indicating excellent predictive accuracy. Compared with existing empirical and standalone models, the proposed framework showed substantial performance gains, up to 83% over SVR, 53% over ANN, and 15% over RF. Sensitivity analysis revealed that HARHA content and maximum dry density positively influence UCS, while clay activity and plasticity-related parameters have negative effects. Overall, the proposed approach offers a robust and sustainable alternative to conventional empirical methods, reducing reliance on extensive laboratory testing.