Mean particle size prediction in rock blast fragmentation using neural networks

Abstract

Abstract Multivariate analysis procedures and a neural network methodology are used to predict mean particle size resulting from rock blast fragmentation. A blast data base developed in a previous study is used in the current study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. In the same previous study a hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. In the same study the group memberships were confirmed by the discriminant analysis. A part of this blast data was used in this study to train a single-hidden layer back-propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. Four learning algorithms were considered to train neural network models. Levenberg–Marquardt algorithm turned out to be the best one providing the highest stability and maximum learning speed. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training are used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models is determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models. The developed neural network models and multivariate regression analysis models are suitable for practical use at mines.