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Though machine learning (ML) approaches have proliferated in the mechanical properties prediction of cemented paste backfill (CPB), their applications have not reached the peak potential due to the lack of more robust techniques. In the present contribution, the state-of-the-art ensemble learning method was employed for improved estimation of the unconfined compressive strength (UCS) of CPB. 126 UCS tests were conducted on two new tailings to provide an enlarged dataset. Tree-based ML approaches, namely, regression tree (RT), random forest (RF), and gradient boosting regression tree (GBRT), were chosen to be individual ML approaches. The ensemble learning framework was used to combine the optimum individual regressors by means of GBRT. 5-fold cross-validation was used as the validation method and the performance was evaluated using correlation coefficient (<inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>). Hyper-parameters tuning was conducted using particle swarm optimization (PSO). The results show that the best training set size was 70%. PSO was robust in the hyper-parameters tuning since the <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> value between experimental and predicted UCS on the training set was progressively increased. The ensemble learning can be used to improve the UCS prediction of CPB. The <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> values between experimental and predicted UCS obtained by RT, RF, GBRT, the ensemble GBRT regressors were 0.9442, 0.9507, 0.9832, and 0.9837, respectively. The method presented in this study extends recent efforts for UCS prediction of CPB and can significantly accelerate the CPB design.

Ensemble Learning Regression for Estimating Unconfined Compressive Strength of Cemented Paste Backfill