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Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational cost and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) are proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with 6 input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF)) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm was integrated with the RF algorithm to find the optimal hyper-parameters ( [[EQUATION]] and [[EQUATION]] ) of each model and the fitness function, which is the MAE of 10 cross-validations. The performance capacities of the optimal models are assessed using mean absolute error, root mean square error, Pearson correlation coefficient and mean absolute percentage error. Findings demonstrated that the PSO-RF model combining L-S-B-T-PF with MAE of (0.0132, and 0.0568), RMSE of (0.0811, 0.1686), R 2 of (0.9990, 0.9961), and MAPE of (0.0027, and 0.0116) in training and testing phases, respectively, has optimal prediction performance. The optimal PSO-RF models are compared with the classical Artificial Neural Network (ANN), RF, Genetic Programming (GP), and Support Vector Machine (SVM) models that show that the PSO-RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which were 31.2(L), 23.1(S), 27.4(B), 36.6(T), 23.4(PF), and 16.9(SD), respectively.

Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (H), the tunnel advance rate (AR), the EPB earth pressure (EP), the mean SPTN value from the soil crown to the surface (Sm), the mean water content of the soil layer (MC), the mean modulus of elasticity of the soil layer (E), and the grout pressure used for injecting grout into the tail void (GP). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R2) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R2 (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that MC, AR, Sm, and GP have a significant impact on ground settlement prediction based on the GOA-RF model.

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For the fault diagnosis of rolling bearings, it is of great significance to improve the diagnostic accuracy. Therefore, this paper presents a rolling bearing fault diagnosis method which combines Daubechies wavelet (DW) with back propagation neural network (BPNN). Specifically, Daubechies wavelet transform is utilized to decompose the vibration signal of the original data in to different frequency components, which can be implemented to extract more prominent fault features. Then, the extracted features are input into BPNN classification model for fault diagnosis by training and testing. Finally, various experiments are carried out on the rolling bearing dataset of Western Reserve University to verify the effectiveness of this method. The results of this study demonstrate that the proposed method is able to reliably identify different fault categories with higher accuracy in comparison with the FT-BPNN methods based on Fourier transform under different loading conditions, and provides a new and effective method for the fault diagnosis of rolling bearings.