Prediction of the compressive strength of tailings backfill using an EO-LightGBM model: performance comparison and feature importance analysis

Objectives As a green construction material utilizing mine tailings, tailings-based concrete plays a significant role in promoting resource reuse. Its compressive strength is critical for structural safety and long-term durability. Accurate prediction of mechanical properties is essential for mix design and engineering applications. Methods Based on experimental data, this study proposes a predictive model for the 7-day compressive strength of tailings concrete using an EO-LightGBM model, which integrates the Equilibrium Optimizer with the Light Gradient Boosting Machine (LightGBM). The model’s performance is compared with several traditional machine learning methods, including Linear Regression, Support Vector Regression, Random Forest, GBDT, XGBoost, and LightGBM. The experiments used copper tailings with a weight concentration of 77%–85%, and considered variables such as tailings ratio (2.33-9.00) and cement-sand ratio (0.033-0.067). A total of 32 mix designs were developed, and their slump, yield stress, viscosity coefficient, and compressive strength were measured. Results The EO-LightGBM model achieved a mean squared error ( MSE ) of 0.022, root mean square error ( RMSE ) of 0.148, and a coefficient of determination ( R 2 ) of 0.94 on the test set, outperforming all other models in terms of prediction accuracy and generalization capability. Feature importance analysis showed that weight concentration, tailings ratio, and cement-sand ratio were the most influential factors on compressive strength, while yield stress and viscosity coefficient had relatively minor contributions. Conclusion The proposed model offers a reliable theoretical and technical basis for the design and strength prediction of tailings-based concrete materials.