Prediction of surface blast patterns in limestone quarries using artificial neural networks

Authors: Tawadrou, A.; Katsabani, P.

Source: Fragblast, Volume 9, Number 4, December 2005 , pp. 233-242(10)

Buy & download fulltext article:

Price: $56.94 plus tax (Refund Policy)

Abstract:

This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry.

Keywords: Blasting; Blast pattern design; Neural networks; Bench blasting

Document Type: Research article

DOI: http://dx.doi.org/10.1080/13855140600761863

Affiliations: 1: Department of Mining Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada

Publication date: 2005-12-01