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Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

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Abstract

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.

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Authors and Affiliations

  1. Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Daneshgah Blvd, P.O. Box 3619995161, Shahrood, Iran

    Ebrahim Ghasemi, Hasel Amini, Mohammad Ataei & Reza Khalokakaei

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  1. Ebrahim Ghasemi
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  2. Hasel Amini
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  3. Mohammad Ataei
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  4. Reza Khalokakaei
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Correspondence to Ebrahim Ghasemi.

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Ghasemi, E., Amini, H., Ataei, M. et al. Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7, 193–202 (2014). https://doi.org/10.1007/s12517-012-0703-6

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  • DOI: https://doi.org/10.1007/s12517-012-0703-6

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