NDLI: Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Content Provider	Springer Nature Link
Author	Saghatforoush, Amir Monjezi, Masoud Shirani Faradonbeh, Roohollah Jahed Armaghani, Danial
Copyright Year	2015
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.
Starting Page	255
Ending Page	266
Page Count	12
File Format	PDF
ISSN	01770667
Journal	Engineering with Computers
Volume Number	32
Issue Number	2
e-ISSN	14355663
Language	English
Publisher	Springer London
Publisher Date	2015-09-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Blasting Flyrock Back-break Artificial neural network Ant colony optimization Computer-Aided Engineering (CAD, CAE) and Design Math. Applications in Chemistry Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Mechanics ApplicationMathematics/Computational Methods of Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Computer Science Applications Modeling and Simulation Software

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