Paper Info
Title
Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting.
Abstract
Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.
Year
DOI
Venue
2016
10.1007/s00366-015-0415-0
Engineering With Computers
Keywords
Field
DocType
Blasting, Flyrock, Back-break, Artificial neural network, Ant colony optimization
Ant colony optimization algorithms,Mathematical optimization,Rock mass classification,Explosive material,Mean squared error,Rock blasting,Artificial neural network,Mathematics,Approximation error,Metaheuristic
Journal
Volume
Issue
ISSN
32
2
1435-5663
Citations 
PageRank 
References 
28
1.46
12
Authors
4
Name
Order
Citations
PageRank
Amir Saghatforoush1281.46
M. Monjezi227119.36
roohollah shirani faradonbeh31409.21
danial jahed armaghani458536.46