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


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Article Info

DATE

2015-01-30

AUTHORS

Ebrahim Ebrahimi, Masoud Monjezi, Mohammad Reza Khalesi, Danial Jahed Armaghani

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. More... »

PAGES

27-36

References to SciGraph publications

  • 2012-06-12. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method in ROCK MECHANICS AND ROCK ENGINEERING
  • 2012-11-09. Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting in ENGINEERING WITH COMPUTERS
  • 2010-03-10. Prediction of rock fragmentation due to blasting using artificial neural network in ENGINEERING WITH COMPUTERS
  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2014-10-18. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2010-08-11. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach in ARABIAN JOURNAL OF GEOSCIENCES
  • 2014-03-14. Flyrock in bench blasting: a comprehensive review in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2014-09-04. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2013-11-16. Investigation of blast-induced ground vibrations in the Tülü boron open pit mine in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2013-09-01. Artificial Neural Network as a Tool for Backbreak Prediction in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2014-07-10. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2012-02-18. Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s10064-015-0720-2

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    http://dx.doi.org/10.1007/s10064-015-0720-2

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