Staff View: Prediction of flyrock in boulder blasting by using artificial neural network

Prediction of flyrock in boulder blasting by using artificial neural network

Rock mass is blasted to break it into smaller pieces such as in most surface mining, quarrying operation, dimensional stone mining and some civil engineering application. Flyrock is one of the most hazardous side effects of blasting operation in surface mining. This phenomenon can be considered as t...

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Main Authors:	Mohamad, Edy Tonnizam, Armaghani, Danial Jahed, Noorani, Seyed Ahmad, Saad, Rosli, Nezhad Khaili Abad, Seyed Vahid Alavi
Format:	Article
Published:	EJGE 2012
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/33468/ http://www.ejge.com/2012/Ppr12.219alr.pdf
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id	my.utm.33468
record_format	eprints
spelling	my.utm.334682019-01-28T03:50:20Z http://eprints.utm.my/id/eprint/33468/ Prediction of flyrock in boulder blasting by using artificial neural network Mohamad, Edy Tonnizam Armaghani, Danial Jahed Noorani, Seyed Ahmad Saad, Rosli Nezhad Khaili Abad, Seyed Vahid Alavi TA Engineering (General). Civil engineering (General) Rock mass is blasted to break it into smaller pieces such as in most surface mining, quarrying operation, dimensional stone mining and some civil engineering application. Flyrock is one of the most hazardous side effects of blasting operation in surface mining. This phenomenon can be considered as the main cause of casualties and damages. The aim of this study is to compare the actual distance of flyrock with the prediction suggested by empirical methods and by using Artificial Neural Network. In addition, this study is also aimed to investigate the most significant input parameters that affecting the flyrock. During this study, flyrock projections for 16 granitic boulders were monitored at Ulu Tiram-quarry site. Blasting parameters such as amount of explosive used, burden, stemming, hole depth, hole angle and hole diameter were carefully measured and recorded. By using these data and applying MATLAB (Matrix Laboratory) program (neural network toolbox), the flyrock distances were predicted for similar condition. The result shows that the coefficient of correlation between the actual and the predicted flyrock distance based on empirical methods is insignificant that is around 0.2. However the result revealed that the coefficient of correlation for overall analysis of flyrock distance is 0.92 based on ANN method. Based on Max-Min method powder factor, stemming and charge length are the most significant parameters in controlling the flyrock distance. This study found that ANN method produced a more accurate prediction than the empirical methods in assessing the actual flyrock projection EJGE 2012 Article PeerReviewed Mohamad, Edy Tonnizam and Armaghani, Danial Jahed and Noorani, Seyed Ahmad and Saad, Rosli and Nezhad Khaili Abad, Seyed Vahid Alavi (2012) Prediction of flyrock in boulder blasting by using artificial neural network. Electronic Journal of Geotechnical Engineering, 17 R . pp. 2585-2595. ISSN 1089-3032 http://www.ejge.com/2012/Ppr12.219alr.pdf
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Mohamad, Edy Tonnizam Armaghani, Danial Jahed Noorani, Seyed Ahmad Saad, Rosli Nezhad Khaili Abad, Seyed Vahid Alavi Prediction of flyrock in boulder blasting by using artificial neural network
description	Rock mass is blasted to break it into smaller pieces such as in most surface mining, quarrying operation, dimensional stone mining and some civil engineering application. Flyrock is one of the most hazardous side effects of blasting operation in surface mining. This phenomenon can be considered as the main cause of casualties and damages. The aim of this study is to compare the actual distance of flyrock with the prediction suggested by empirical methods and by using Artificial Neural Network. In addition, this study is also aimed to investigate the most significant input parameters that affecting the flyrock. During this study, flyrock projections for 16 granitic boulders were monitored at Ulu Tiram-quarry site. Blasting parameters such as amount of explosive used, burden, stemming, hole depth, hole angle and hole diameter were carefully measured and recorded. By using these data and applying MATLAB (Matrix Laboratory) program (neural network toolbox), the flyrock distances were predicted for similar condition. The result shows that the coefficient of correlation between the actual and the predicted flyrock distance based on empirical methods is insignificant that is around 0.2. However the result revealed that the coefficient of correlation for overall analysis of flyrock distance is 0.92 based on ANN method. Based on Max-Min method powder factor, stemming and charge length are the most significant parameters in controlling the flyrock distance. This study found that ANN method produced a more accurate prediction than the empirical methods in assessing the actual flyrock projection
format	Article
author	Mohamad, Edy Tonnizam Armaghani, Danial Jahed Noorani, Seyed Ahmad Saad, Rosli Nezhad Khaili Abad, Seyed Vahid Alavi
author_facet	Mohamad, Edy Tonnizam Armaghani, Danial Jahed Noorani, Seyed Ahmad Saad, Rosli Nezhad Khaili Abad, Seyed Vahid Alavi
author_sort	Mohamad, Edy Tonnizam
title	Prediction of flyrock in boulder blasting by using artificial neural network
title_short	Prediction of flyrock in boulder blasting by using artificial neural network
title_full	Prediction of flyrock in boulder blasting by using artificial neural network
title_fullStr	Prediction of flyrock in boulder blasting by using artificial neural network
title_full_unstemmed	Prediction of flyrock in boulder blasting by using artificial neural network
title_sort	prediction of flyrock in boulder blasting by using artificial neural network
publisher	EJGE
publishDate	2012
url	http://eprints.utm.my/id/eprint/33468/ http://www.ejge.com/2012/Ppr12.219alr.pdf
_version_	1643649340138323968
score	13.211869

Prediction of flyrock in boulder blasting by using artificial neural network

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