Prediction of Fly-rock using Gene Expression Programming and Teaching– learning-based Optimization Algorithm

This work attempts to estimate the amount of fly-rock in the Angoran mine in the Zanjan province (Iran) using the gene expression programming (GEP) predictive technique. For this, the input data including the fly-rock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster is collected from the mine. Then using GEP, a series of intelligent equations are proposed in order to predict the fly-rock distance. The best GEP equation is selected based on some well- established statistical indices in the next stage. The coefficient of determination for the training and testing datasets of the GEP equation are 0.890 and 0.798, respectively. The model obtained from the GEP method is then optimized using the teaching– learning-based optimization (TLBO) algorithm. Based on the results obtained, the correlation coefficient of the training and testing data increase to 91% and 89%, which increase the accuracy of the equation. This new intelligent equation could forecast fly- rock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues.