Predicting backbreak due to blasting using LSSVM optimized by metaheuristic algorithms

Abstract

Backbreak is an undesirable outcome in blasting operations caused by factors such as equipment failure, improper fragmentation, unstable mine walls, reduced drilling efficiency, and other issues that contribute to increased mining operation costs. To overcome these problems effectively, this study developed a least square support vector machine (LSSVM) model and optimized it using metaheuristic algorithms, including genetic algorithm (GA)-LSSVM, particle swarm optimization (PSO)-LSSVM, and grey wolf optimization (GWO)-LSSVM, to predict the efficiency and accuracy of backbreak due to blasting in surface mines using burden (m), spacing (m), stemming (m), powder factor (kg/m^s), and stiffness ratio (m/m) as input parameters. Among the models evaluated, the GWO-LSSVM model demonstrated superior performance compared to the LSSVM, GA-LSSVM, and PSO-LSSVM models, achieving a coefficient of determination of 0.998 and 0.997, mean absolute error of 0.0068 and 0.1209, root mean squared error of 0.0825 and 0.1936, and SI of 0.021 and 0.044 on the training and testing datasets, respectively. Sensitivity analysis of the GWO-LSSVM model revealed that the powder factor exerted the most significant influence, while the burden had the least impact on backbreak. This developed method has proven to significantly enhance the performance evaluation of backbreak in surface mines and offers valuable engineering applications in mining and other related fields.