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

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 a...

Full description

Saved in:
Bibliographic Details
Published in:Arabian journal of geosciences 2014, Vol.7 (1), p.193-202
Main Authors: Ghasemi, Ebrahim, Amini, Hasel, Ataei, Mohammad, Khalokakaei, Reza
Format: Article
Language:English
Subjects:
Earth and Environmental Science
Earth Sciences
Original Paper
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-012-0703-6