TY - JOUR N2 - In this paper, an attempt was made to find out two empirical relationships incorporating linear multivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable. L1 - http://journals.pan.pl/Content/116362/PDF/Archiwum-65-2-09-Dehghani.pdf L2 - http://journals.pan.pl/Content/116362 PY - 2020 IS - No 2 EP - 355 DO - 10.24425/ams.2020.133195 KW - blasting KW - ground vibrations KW - Gene expression programing KW - Linear multivariate regression KW - Sarcheshme copper mine A1 - Shakeri, Jamshid A1 - Shokri, Behshad Jodeiri A1 - Dehghani, Hesam PB - Committee of Mining PAS VL - vol. 65 DA - 2020.06.08 T1 - Prediction of Blast-Induced Ground Vibration Using Gene Expression Programming (GEP), Artificial Neural Networks (ANNs), and Linear Multivariate Regression (LMR) SP - 317 UR - http://journals.pan.pl/dlibra/publication/edition/116362 T2 - Archives of Mining Sciences ER -