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Optimized data-driven models for prediction of flyrock due to blasting in surface mines

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce th...

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Main Authors: Ding, Xiaohua, Jamei, Mehdi, Hasanipanah, Mahdi, Abdullah, Rini Asnida, Le, Binh Nguyen
Format: Article
Language:English
Published: MDPI 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/107339/1/RiniAsnidaAbdullah2023_OptimizedDataDrivenModelsforPrediction.pdf
http://eprints.utm.my/107339/
http://dx.doi.org/10.3390/su15108424
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spelling my.utm.1073392024-09-03T06:16:32Z http://eprints.utm.my/107339/ Optimized data-driven models for prediction of flyrock due to blasting in surface mines Ding, Xiaohua Jamei, Mehdi Hasanipanah, Mahdi Abdullah, Rini Asnida Le, Binh Nguyen TA Engineering (General). Civil engineering (General) Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy. MDPI 2023-05 Article PeerReviewed application/pdf en http://eprints.utm.my/107339/1/RiniAsnidaAbdullah2023_OptimizedDataDrivenModelsforPrediction.pdf Ding, Xiaohua and Jamei, Mehdi and Hasanipanah, Mahdi and Abdullah, Rini Asnida and Le, Binh Nguyen (2023) Optimized data-driven models for prediction of flyrock due to blasting in surface mines. Sustainability (Switzerland), 15 (10). pp. 1-20. ISSN 2071-1050 http://dx.doi.org/10.3390/su15108424 DOI:10.3390/su15108424
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/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ding, Xiaohua
Jamei, Mehdi
Hasanipanah, Mahdi
Abdullah, Rini Asnida
Le, Binh Nguyen
Optimized data-driven models for prediction of flyrock due to blasting in surface mines
description Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.
format Article
author Ding, Xiaohua
Jamei, Mehdi
Hasanipanah, Mahdi
Abdullah, Rini Asnida
Le, Binh Nguyen
author_facet Ding, Xiaohua
Jamei, Mehdi
Hasanipanah, Mahdi
Abdullah, Rini Asnida
Le, Binh Nguyen
author_sort Ding, Xiaohua
title Optimized data-driven models for prediction of flyrock due to blasting in surface mines
title_short Optimized data-driven models for prediction of flyrock due to blasting in surface mines
title_full Optimized data-driven models for prediction of flyrock due to blasting in surface mines
title_fullStr Optimized data-driven models for prediction of flyrock due to blasting in surface mines
title_full_unstemmed Optimized data-driven models for prediction of flyrock due to blasting in surface mines
title_sort optimized data-driven models for prediction of flyrock due to blasting in surface mines
publisher MDPI
publishDate 2023
url http://eprints.utm.my/107339/1/RiniAsnidaAbdullah2023_OptimizedDataDrivenModelsforPrediction.pdf
http://eprints.utm.my/107339/
http://dx.doi.org/10.3390/su15108424
_version_ 1811681165170442240
score 13.211869

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