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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2016

01.04.2016 | Original Article

Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety

verfasst von: M. Yari, R. Bagherpour, S. Jamali, R. Shamsi

Erschienen in: Neural Computing and Applications | Ausgabe 3/2016

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Abstract

One of the threatening safety problems in mines is flyrock distance range through blasting operation. Inaccurate evaluation of flyrock can cause fatal and nonfatal accidents. The presented results in this paper verify efficiency of artificial neural network in prediction of flyrock considering all influencing parameters such as: hole diameter, height, subdrilling, number of holes, spacing, burden, ANFO amount, dynamite weight, stemming, powder factor, specific drilling, and delay time. In this research, optimum structure of network was determined by studying different transfer functions and number of the neurons using a programming code. In this case, optimum structure configuration is logsig transfer functions for the two hidden layers and tansig or logsig one for output, and there are eight neurons in each hidden layers. By calculating strength of relationship between flyrock and all influencing parameters using cosine amplitude method (CAM), the powder factor is defined as most effective parameter on the flyrock.
Vorheriger Artikel Color pathological image encryption scheme with S-boxes generated by complex chaotic system and environmental noise
Nächster Artikel An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

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Metadaten
Titel
Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety
verfasst von
M. Yari
R. Bagherpour
S. Jamali
R. Shamsi
Publikationsdatum
01.04.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1889-9

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