TY - JOUR
ID - 1662
TI - Prediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks
JO - Journal of Mining and Environment
JA - JME
LA - en
SN - 2251-8592
AU - Bastami, R.
AU - Aghajani Bazzazi, A.
AU - Hamidian Shoormasti, H.
AU - Ahangari, K.
AD - Department of Mining Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AD - Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.
AD - Department of Mining Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.
Y1 - 2020
PY - 2020
VL - 11
IS - 1
SP - 281
EP - 300
KW - Blasting Cost
KW - Limestone Mines
KW - Gene Expression
KW - Nonlinear Multivariate Regression
KW - Artificial Neural Network
DO - 10.22044/jme.2019.9027.1790
N2 - The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (GEP), linear multivariate regression (LMR), and non-linear multivariate regression (NLMR) models. In all models, the ANFO value, number of detonators, Emolite value, hole number, hole length, hole diameter, burden, spacing, stemming, sub-drilling, specific gravity of rock, hardness, and uniaxial compressive strength are used as the input parameters. The ANN model results in the test stage indicating a higher correlation coefficient (0.954) and a lower root mean square error (973) compared to the other models. In addition, it has a better conformity with the real blasting costs in comparison with the other models. Although the ANNs method is regarded as one of the intelligent and powerful techniques in parameter prediction, its most important fault is its inability to provide mathematical equations for engineering operations. In contrast, the GEP model exhibits a reliable output by presenting a mathematical equation for BC prediction with a correlation coefficient of 0.933 and a root mean square error of 1088. Based on the sensitivity analysis, the spacing and ANFO values have the maximum and minimum effects on the BC function, respectively. The number of detonators, Emolite value, hole number, specific gravity, hardness, and rock uniaxial compressive strength have a positive correlation with BC, while the ANFO value, hole length, hole diameter, burden, spacing, stemming, and sub-drilling have a negative correlation with BC.
UR - https://jme.shahroodut.ac.ir/article_1662.html
L1 - https://jme.shahroodut.ac.ir/article_1662_bb00c3504b75c713123dce0b8a385b87.pdf
ER -