Blasted muckpile modeling in open pit mines using an artificial neural network designed by genetic algorithm

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Mirabedi S. M. Mahdi; Rahmanpour Mehdi; Azimi Yousef; Bakhshandeh Amnieh Hassan

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Journal Paper

Paper Information

Journal: International Journal of Mining and Geo-Engineering Year:2024 | Volume:58 | Issue:2 Page(s): 211-220

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Information Journal Paper

Title

Blasted muckpile modeling in open pit mines using an artificial neural network designed by genetic algorithm

Author(s)

Mirabedi S. M. Mahdi | Rahmanpour Mehdi | Azimi Yousef | Bakhshandeh Amnieh Hassan | Issue Writer Certificate

Keywords

Hybrid genetic algorithm neural network

blasting

muckpile

Principal component analysis

Abstract

The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the Principal component analysis. Finally, the best models for predicting throw and drop are determined. Analyzing the performance of the proposed models indicates their superiority in predicting muckpile shape. As a result, the Mean Squared Error of throw is 0.53 for train data and 1.24 for test data. While for the drop, the errors are 0.45 and 0.58 for the training and testing data. Furthermore, sensitivity analysis shows that specific-charge effects drop and throw more.

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