Evaluating blast-induced backbreak in open pit mines using the LSSVM optimized by the GWO algorithm

Backbreak, a recurring issue in blasting operations, causes mine wall instability, equipment failure, inappropriate disintegration, lower drilling efficiency, and increased cost of mining operations. This study aims to address these issues by developing a hybrid LSSVM-GWO model for predicting blast-induced backbreak in open pit mines. To evaluate the effectiveness of the proposed model, its predictive performance was compared with three convolutional models, such as the support vector machine, K-nearest neighbor, and the least square support vector machine. Results demonstrated that the LSSVM-GWO model outperformed the other three models, achieving coefficient of determination values of 0.998 and 0.997, mean absolute error values of 0.0068 and 0.1209, root mean squared error values of 0.0825 and 0.1936, and a20-index values of 0.99 and 1.01 for training and testing datasets, respectively. Furthermore, the SHAP machine learning technique was applied to evaluate the feature importance, revealing that the powder factor had the highest influence, while the burden exhibited the least impact on backbreak. Sensitivity analysis confirmed these findings, highlighting the robustness of the hybrid model. The study concludes that the LSSVM-GWO model significantly enhances the prediction and evaluation of backbreak in open pit mines, providing critical insights to improve blasting operations, reduce costs, and ensure mine safety.