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Classification and regression tree technique in estimating peak particle velocity caused by blasting

Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads...

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Main Authors:	Khandelwal, M., Armaghani, D. J., Faradonbeh, R. S., Yellishetty, M., Majid, M. Z. A., Monjezi, M.
Format:	Article
Published:	Springer-Verlag London 2017
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/80340/ http://dx.doi.org/10.1007%2Fs00366-016-0455-0
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id	my.utm.80340
record_format	eprints
spelling	my.utm.803402019-05-10T07:16:31Z http://eprints.utm.my/id/eprint/80340/ Classification and regression tree technique in estimating peak particle velocity caused by blasting Khandelwal, M. Armaghani, D. J. Faradonbeh, R. S. Yellishetty, M. Majid, M. Z. A. Monjezi, M. TA Engineering (General). Civil engineering (General) Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads and slopes in the vicinity. Therefore, proper prediction of blast-induced ground vibrations is essential to demarcate the safety area of blasting. In this research, classification and regression tree (CART) as a rule-based method was used to predict the peak particle velocity through a database comprising of 51 datasets with results of maximum charge per delay and distance from the blast face were fixed as model inputs. For comparison, the empirical and multiple regression (MR) models were also applied and proposed for peak particle velocity prediction. Performance of the proposed models were compared and evaluated using three statistical criteria, namely coefficient of correlation (R2), root mean square error (RMSE) and variance account for (VAF). Comparison of the obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field. Springer-Verlag London 2017 Article PeerReviewed Khandelwal, M. and Armaghani, D. J. and Faradonbeh, R. S. and Yellishetty, M. and Majid, M. Z. A. and Monjezi, M. (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Engineering with Computers, 33 (1). pp. 45-53. ISSN 0177-0667 http://dx.doi.org/10.1007%2Fs00366-016-0455-0 DOI:10.1007%2Fs00366-016-0455-0
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) Khandelwal, M. Armaghani, D. J. Faradonbeh, R. S. Yellishetty, M. Majid, M. Z. A. Monjezi, M. Classification and regression tree technique in estimating peak particle velocity caused by blasting
description	Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads and slopes in the vicinity. Therefore, proper prediction of blast-induced ground vibrations is essential to demarcate the safety area of blasting. In this research, classification and regression tree (CART) as a rule-based method was used to predict the peak particle velocity through a database comprising of 51 datasets with results of maximum charge per delay and distance from the blast face were fixed as model inputs. For comparison, the empirical and multiple regression (MR) models were also applied and proposed for peak particle velocity prediction. Performance of the proposed models were compared and evaluated using three statistical criteria, namely coefficient of correlation (R2), root mean square error (RMSE) and variance account for (VAF). Comparison of the obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field.
format	Article
author	Khandelwal, M. Armaghani, D. J. Faradonbeh, R. S. Yellishetty, M. Majid, M. Z. A. Monjezi, M.
author_facet	Khandelwal, M. Armaghani, D. J. Faradonbeh, R. S. Yellishetty, M. Majid, M. Z. A. Monjezi, M.
author_sort	Khandelwal, M.
title	Classification and regression tree technique in estimating peak particle velocity caused by blasting
title_short	Classification and regression tree technique in estimating peak particle velocity caused by blasting
title_full	Classification and regression tree technique in estimating peak particle velocity caused by blasting
title_fullStr	Classification and regression tree technique in estimating peak particle velocity caused by blasting
title_full_unstemmed	Classification and regression tree technique in estimating peak particle velocity caused by blasting
title_sort	classification and regression tree technique in estimating peak particle velocity caused by blasting
publisher	Springer-Verlag London
publishDate	2017
url	http://eprints.utm.my/id/eprint/80340/ http://dx.doi.org/10.1007%2Fs00366-016-0455-0
_version_	1643658384112615424
score	13.211869

Classification and regression tree technique in estimating peak particle velocity caused by blasting

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