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 (R). 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 R 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 R 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.

Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects.