Author/Authors :
Shamsi, Reza Department of Mining Engineering - Hamedan University of Technology, Hamedan, Iran , Amini, Mohammad Saeed Department of Mining Engineering - Amirkabir University, Tehran, Iran , Dehghani, Hesam Department of Mining Engineering - Hamedan University of Technology, Hamedan, Iran , Bascompta, Marc Polytechnic University of Catalonia, Catalonia, Spain , Jodeiri Shokri, Behshad School of Civil Engineering and Surveying - University of Southern Queensland, Queensland, Australia , Entezam, Shima School of Civil Engineering and Surveying - University of Southern Queensland, Queensland, Australia
Abstract :
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.
Keywords :
Blasting operations , Fly-rock , Gene expression programing , Teaching–learning-based , optimization algorithm
