R. Shamsi - Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
M. S. Amini - Department of Mining Engineering, Amirkabir University, Tehran, Iran
H. Dehghani - Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
M. Bascompta - Polytechnic University of Catalonia, Catalonia, Spain
B. Jodeiri Shokri - School of Civil Engineering and Surveying, University of Southern Queensland, Queensland, Australia
Sh. Entezam - School of Civil Engineering and Surveying, University of Southern Queensland, Queensland, Australia

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 ۰.۸۹۰ and ۰.۷۹۸, 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 ۹۱% and ۸۹%, 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.

Blasting Operations, Flyrock, Gene expression programing, Teaching – learning-based optimization algorithm