prediction of fly-rock using gene expression programming and teaching–learning-based optimization algorithm

This paper attempted to estimate the amount of flyrock in the angoran mine in zanjan province, iran using the gene expression programming (gep) predictive technique. the input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were collected from the mine. then, using gep, a series of intelligent equations were proposed to predict flyrock distance. the best gep equation was selected based on some well-established statistical indices in the next stage. the coefficient of determination for training and testing datasets of the gep equation were 0.890 and 0.798, respectively. the model obtained from the gep method was then optimized using teaching– learning-based optimization algorithm (tlbo). based on the results, the correlation coefficient of training and testing data increased to 91% and 89%, which increased the accuracy of the equation. this new intelligent equation could forecast flyrock 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.