Enhancing the Performance of LSSVM Model in Predicting Rock Fragmentation Size Via Optimization Algorithms

Abstract

The prediction of rock fragmentation is critical to improve the efficiency and economy of blasting excavation. In this study, an attempt is made to predict the entire fragmentation size distribution using a least square support vector machine (LSSVM) model. In addition, three optimization algorithms—the bacterial foraging algorithm (BFO), artificial fish swarm algorithm (AFSA), and adaptive particle swarm optimization (APSO)–were used to determine the appropriate parameters of the LSSVM model. In the constructed LSSVM-BFO, LSSVM-AFSA and LSSVM-APSO models, the hole spacing, row spacing, change per delay and stemming were used as the input parameters, while the statistical rock fragmentation size was assigned as the output. The LSSVM model was also employed as a control group for comparing with the optimized models. The above-mentioned models were trained and tested based on a database comprising of 10 datasets collected from in-site testing of Altashi Water Control Project in China. The performance of the proposed models was compared by several statistical criteria. The viability and efficiency of the LSSVM-BFO model were confirmed with an R2 of 0.9960 and an RMSE of 1.8044, which were better than those of the LSSVM-AFSA, LSSVM-APSO and LSSVM. Last but not least, sensitivity analysis was also executed. The result of sensitivity analysis demonstrated when the size of rock fragmentation of prediction is less 80mm, the most effective parameter will be stemming; otherwise the most effective parameter will be hole spacing.