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Engineering with Computers
Erschienen in: Engineering with Computers 2/2011

01.04.2011 | Original Article

Prediction of rock fragmentation due to blasting using artificial neural network

verfasst von: A. Bahrami, M. Monjezi, K. Goshtasbi, A. Ghazvinian

Erschienen in: Engineering with Computers | Ausgabe 2/2011

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Abstract

Prediction of rock fragmentation is essential for optimizing blasting operation. Fragmentation depends on many parameters such as rock mass properties, blast geometry and explosive properties. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation due to blasting in an iron ore mine. In the developing of the proposed model eight parameters such as hole diameter, burden, powder factor, blastability index, etc., were incorporated. Training of the model was performed by back-propagation algorithm using 220 datasets. A four-layer ANN was found to be optimum with architecture 10-9-7-1. Sensitivity analysis revealed that the most effective parameters on rock fragmentation are blastability index (G), charge per delay (J), burden (C), SMR (F) and powder factor (E).
Vorheriger Artikel Implementing surfactant mass balance in 2D FEM–ALE models
Nächster Artikel A theoretical framework for an intelligent design catalogue

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Metadaten
Titel
Prediction of rock fragmentation due to blasting using artificial neural network
verfasst von
A. Bahrami
M. Monjezi
K. Goshtasbi
A. Ghazvinian
Publikationsdatum
01.04.2011
Verlag
Springer-Verlag
Erschienen in
Engineering with Computers / Ausgabe 2/2011
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-010-0187-5

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