Ataei, M., Sereshki, F. (2017). Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm. Journal of Mining and Environment, 8(2), 291-304. doi: 10.22044/jme.2016.654
M. Ataei; F. Sereshki. "Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm". Journal of Mining and Environment, 8, 2, 2017, 291-304. doi: 10.22044/jme.2016.654
Ataei, M., Sereshki, F. (2017). 'Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm', Journal of Mining and Environment, 8(2), pp. 291-304. doi: 10.22044/jme.2016.654
Ataei, M., Sereshki, F. Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm. Journal of Mining and Environment, 2017; 8(2): 291-304. doi: 10.22044/jme.2016.654
Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm
School of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Abstract
Like most limestone mines, which produce the raw materials required for cement companies, the transportation cost of the raw materials used in the Shahrood Cement Company is high. It has been tried to build the crushing and grinding plant close to the mine as much as possible. On the other hand, blasting has harmful effects, and the impacts of blast-induced damages on the sensitive machinery, equipment, and buildings are considerable. In such mines, among the blasting effects, blast-induced vibrations have a great deal of importance. This research work was conducted to analyze the blasting effects, and to propose a valid and reliable formula to predict the blast-induced vibration impacts in such regions, especially for the Shahrood Cement Company. Up to the present time, different indices have been introduced to quantify the blast vibration effects, among which peak particle velocity (PPV) has been widely considered by a majority of researchers. In order to establish a relationship between PPV and the blast site properties, different formulas have been proposed till now, and their frequently-used versions have been employed in the general form of , where W and D are the maximum charge per delay and the distance from the blast site, respectively, and , , and describe the site specifications. In this work, a series of tests and field measurements were carried out, and the required parameters were collected. Then in order to generalize the relationship between different limestone mines, and also to increase the prediction precision, the related data for similar limestone mines was gathered from the literature. In order to find the best equation fitting the real data, a simple regression model with genetic algorithm was used, and the best PPV predictor was achieved. At last, the results obtained for the best predictor model were compared with the real measured data by means of a correlation analysis.
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