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The Optimization of Statistical Models for Predicting Blast-induced Back-break in Mining using the Firefly Algorithm: A Case Study

Document Type : Original Research Paper

Authors

1 Faculty of Mining, Petroleum & Geophysics Eng., Shahrood University of Technology, Shahrood, Iran

2 Department of Mining and Geology of Research and Technology Management of Gol-e-Gohar, Sirjan, Iran

10.22044/jme.2025.15683.3014

Abstract

In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and disruption in drilling and charging operations in subsequent stages. The objective of this research is to predict and optimize back-break by combining statistical models with the Firefly Algorithm (FA). For this purpose, a database comprising data from 28 blasts in the waste rock section of Gol-e-Gohar Iron Ore Mine No. 1 was compiled. After data collection, the input parameters, including blast hole length, burden, spacing, Stemming, charge per delay, and Number of holes in the last row, were identified and utilized in the modeling process. To predict back-break, modeling was performed using multiple regression analysis. Among the developed models, the Polynomial statistical model with non-integer coefficients model with an adjusted coefficient of determination 0.885 was identified as the best-performing model and was subsequently used as the objective function in the Firefly Algorithm. The optimization process was then carried out using this algorithm. According to the findings of this research, the implementation of the current operational patterns in the mine along with the optimized proposed patterns resulted in a reduction of 4 meters in the average back-break, decreasing it from 7.5 meters in the waste rock section. The results demonstrate that the Firefly Algorithm is a highly effective and reliable tool for model optimization and a more accurate reduction of back-breaks. This approach has the potential to significantly enhance the efficiency of mining operations and reduce operational costs.

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References
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Journal of Mining and Environment
Volume 17, Issue 3
May and June 2026
Pages 1051-1064
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