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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2017

13.01.2016 | Original Article

Particle swarm optimization approach for forecasting backbreak induced by bench blasting

verfasst von: Ebrahim Ghasemi

Erschienen in: Neural Computing and Applications | Ausgabe 7/2017

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Abstract

One of the most challenging safety problems in open pit mines is backbreak during blasting operation, and its prediction is very important for a technically and economically successful mining operation. This paper presents application of particle swarm optimization (PSO) technique to estimate the backbreak induced by bench blasting, based on major controllable blasting parameters. Two forms of PSO models, linear and quadratic, are developed based on blasting data from Sungun copper mine, Iran. According to obtained results, both models can be used to predict the backbreak, but the comparison of two models, in terms of statistical performance indices, shows that the quadratic form provides better results than the linear form.
Vorheriger Artikel Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach
Nächster Artikel Cloud computing system risk estimation and service selection approach based on cloud focus theory

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Metadaten
Titel
Particle swarm optimization approach for forecasting backbreak induced by bench blasting
verfasst von
Ebrahim Ghasemi
Publikationsdatum
13.01.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2182-2

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