Combined ANN Prediction Model for Rock Fragmentation Distribution due to Blasting

Abstract

It is feasible using Artificial Neural Network (ANN) to predict the distribution of particle size in bench blasting. It explored the minimum error based on the steepest descent method and plunged into local minima easily. A Back-propagation Neural Network (BPNN) improved by genetic algorithm (GA-BP) to predict mean particle size (K50) was proposed. The input variables used for the proposed method were rock joints (J), Protogyakonov’s coefficient (f ), burden (W ), time interval of millisecond (T ), detonation velocity (V ), explosive specific charge (G), stemming length (L) and hole-space (S). After using the practical data for training and testing, the forecasted mean Relative Errors (RE) of Multivariate Regression Analysis (MVRA), BPNN were 11:15% and 7:38% respectively while the RE achieved by GA-BP was 3:09%. Results indicate that GA-BP model can efficiently reach the expected target, and the problem of incidental trap in local minimum is solved. The improved model is more suitable for prediction of blasting fragmentation.