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Engineering with Computers
Erschienen in: Engineering with Computers 2/2016

01.04.2016 | Original Article

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

verfasst von: Amir Saghatforoush, Masoud Monjezi, Roohollah Shirani Faradonbeh, Danial Jahed Armaghani

Erschienen in: Engineering with Computers | Ausgabe 2/2016

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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.
Vorheriger Artikel Damage identification through the comparison with pseudo-baseline data at damaged state
Nächster Artikel A combined scheme of edge-based and node-based smoothed finite element methods for Reissner–Mindlin flat shells

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Metadaten
Titel
Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting
verfasst von
Amir Saghatforoush
Masoud Monjezi
Roohollah Shirani Faradonbeh
Danial Jahed Armaghani
Publikationsdatum
01.04.2016
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 2/2016
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-015-0415-0

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