Prediction of Unconfined Compressive Strength of Cemented Tailings Backfill Containing Coarse Aggregate Using a Hybrid Model Based on Extreme Gradient Boosting

Abstract

The utilization of cemented tailings backfill (CTB) presents distinct advantages in managing tailings and underground mining voids, occasionally incorporating coarse aggregate. In this study, the particle swarm optimization (PSO) algorithm was employed to optimize the extreme gradient boosting (XGBoost) model for predicting the unconfined compressive strength (UCS) of CTB containing coarse aggregate (CTBCA). Additionally, feature importance was compared and analyzed. The findings indicate that the PSO‐XGBoost model exhibits high accuracy on the test set, with a root mean square error (RMSE) of 0.091, a mean square error (MSE) of 0.008, and a coefficient of determination (R2) of 0.999. The predicted values demonstrate a high degree of consistency with the actual results, exhibiting minimal errors that follow a normal distribution. The feature importance analysis reveals that the cement‐sand ratio holds the highest importance score and exerts a significant influence on the UCS prediction. In descending order of impact, the next most significant factors are curing age, slurry concentration, and the coarse aggregate ratio. The proposed PSO‐XGBoost model effectively reduces the UCS measurement cycle while maintaining prediction accuracy. Thus, this model has the potential to provide a fast and efficient method for predicting the UCS of CTBCA.