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Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

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Abstract

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.

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Authors and Affiliations

  1. Department of Mining, Faculty of Engineering, Tarbiat Modares University, 14115-143, Tehran, Iran

    Ebrahim Ebrahimi, Masoud Monjezi & Mohammad Reza Khalesi

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

    Danial Jahed Armaghani

Authors
  1. Ebrahim Ebrahimi
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  2. Masoud Monjezi
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  3. Mohammad Reza Khalesi
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  4. Danial Jahed Armaghani
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Correspondence to Masoud Monjezi.

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Ebrahimi, E., Monjezi, M., Khalesi, M.R. et al. Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75, 27–36 (2016). https://doi.org/10.1007/s10064-015-0720-2

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  • DOI: https://doi.org/10.1007/s10064-015-0720-2

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