Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model

The uniaxial compressive strength of cemented paste backfill (CPB), as an important indicator of their mechanical properties, is usually determined by traditional mechanical tests. In the proposed model, the whale optimization algorithm (WOA) with global optimization capacity was used to tune the hyperparameters of the extreme gradient boosting (XGBoost) model. Taking the 80 sets of data obtained from the filling slurry ratio test of a lead-zinc mine as the database, the solid mass fraction, cement content, tailings content as well as curing age, were selected as input variables and the uniaxial compressive strength of the filling body as an output variable. XGBoost, random forest (RF) and WOA-RF models were constructed to compare with the WOA-XGBoost model. The results indicates that the hybrid WOA-XGBoost model (Its determination coefficient is 0.965 0, the root mean square error is 0.207 4, and the mean absolute error is 0.170 3) performs rather better than the individual XGBoost model (Its determination coefficient is 0.897 1, the root mean square error is 0.408 4, and the mean absolute error is 0.246 7). Compared with other models, the WOA-XGBoost model exhibits the highest prediction accuracy, contributing to the design and ratio optimization of cemented paste backfill materials.