- Title
- Evaluation of effect of blast design parameters on flyrock using artificial neural networks
- Creator
- Monjezi, Masoud; Mehrdanesh, Amirhossein; Malek, Alaeddin; Khandelwal, Manoj
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/163155
- Identifier
- vital:12778
- Identifier
-
https://doi.org/10.1007/s00521-012-0917-2
- Identifier
- ISBN:0941-0643
- Abstract
- Flyrock, the propelled rock fragments beyond a specific limit, can be considered as one of the most crucial and hazardous events in the open pit blasting operations. Involvement of various effective parameters has made the problem so complicated, and the available empirical methods are not proficient to predict the flyrock. To achieve more accurate results, employment of new approaches, such as artificial neural network (ANN) can be very helpful. In this paper, an attempt has been made to apply the ANN method to predict the flyrock in the blasting operations of Sungun copper mine, Iran. Number of ANN models was tried using various permutation and combinations, and it was observed that a model trained with back-propagation algorithm having 9-5-2-1 architecture is the best optimum. Flyrock were also computed from various available empirical models suggested by Lundborg. Statistical modeling has also been done to compare the prediction capability of ANN over other methods. Comparison of the results showed absolute superiority of the ANN modeling over the empirical as well as statistical models. Sensitivity analysis was also performed to identify the most influential inputs on the output results. It was observed that powder factor, hole diameter, stemming and charge per delay are the most effective parameters on the flyrock. © 2012 Springer-Verlag London Limited.
- Publisher
- Springer Verlag
- Relation
- Neural Computing and Applications Vol. 23, no. 2 (2013), p. 349-356
- Rights
- Copyright © 2012 Springer-Verlag London Limited.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; 1702 Cognitive Science; Artificial neural network; Backpropagation; Blasting; Flyrock; Sungun mine
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