Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting

Abstract

Desired rock fragmentation is the main goal of the blasting operation in surface mines, civil and tunneling works. Therefore, precise prediction of rock fragmentation is very important to achieve an economically successful outcome. The primary objective of this article is to propose a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO). The proposed PSO–ANFIS model has been compared with support vector machines (SVM), ANFIS and nonlinear multiple regression (MR) models. To construct the predictive models, 72 blasting events were investigated, and the values of rock fragmentation as well as five effective parameters on rock fragmentation, i.e., specific charge, stemming, spacing, burden and maximum charge used per delay were measured. Based on several statistical functions [e.g., coefficient of correlation (R 2) and root-mean-square error (RMSE)], it was found that the PSO–ANFIS (with R 2 = 0.89 and RMSE = 1.31) performs better than the SVM (with R 2 = 0.83 and RMSE = 1.66), ANFIS (with R 2 = 0.81 and RMSE = 1.78) and nonlinear MR (with R 2 = 0.57 and RMSE = 3.93) models. Finally, the sensitivity analysis shows that the burden and maximum charge used per delay have the least and the most effects on the rock fragmentation, respectively.