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Application of PSO to develop a powerful equation for prediction of flyrock due to blasting

Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions o...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Hasanipanah, Mahdi, Jahed Armaghani, Danial, Bakhshandeh Amnieh, Hassan, Majid, Muhd. Zaimi Abd., Tahir, Mahmood Md.
التنسيق: مقال
منشور في: Springer-Verlag London Ltd 2016
الموضوعات:
TA Engineering (General). Civil engineering (General)
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/72821/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975166243&doi=10.1007%2fs00521-016-2434-1&partnerID=40&md5=a9106daf2883f913f46f76a56dedb0ff
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id my.utm.72821
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spelling my.utm.728212017-11-20T08:14:58Z http://eprints.utm.my/id/eprint/72821/ Application of PSO to develop a powerful equation for prediction of flyrock due to blasting Hasanipanah, Mahdi Jahed Armaghani, Danial Bakhshandeh Amnieh, Hassan Majid, Muhd. Zaimi Abd. Tahir, Mahmood Md. TA Engineering (General). Civil engineering (General) Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases. Springer-Verlag London Ltd 2016 Article PeerReviewed Hasanipanah, Mahdi and Jahed Armaghani, Danial and Bakhshandeh Amnieh, Hassan and Majid, Muhd. Zaimi Abd. and Tahir, Mahmood Md. (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Computing and Applications . pp. 1-8. ISSN 0941-0643 (In Press) https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975166243&doi=10.1007%2fs00521-016-2434-1&partnerID=40&md5=a9106daf2883f913f46f76a56dedb0ff
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)
Hasanipanah, Mahdi
Jahed Armaghani, Danial
Bakhshandeh Amnieh, Hassan
Majid, Muhd. Zaimi Abd.
Tahir, Mahmood Md.
Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
description Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases.
format Article
author Hasanipanah, Mahdi
Jahed Armaghani, Danial
Bakhshandeh Amnieh, Hassan
Majid, Muhd. Zaimi Abd.
Tahir, Mahmood Md.
author_facet Hasanipanah, Mahdi
Jahed Armaghani, Danial
Bakhshandeh Amnieh, Hassan
Majid, Muhd. Zaimi Abd.
Tahir, Mahmood Md.
author_sort Hasanipanah, Mahdi
title Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
title_short Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
title_full Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
title_fullStr Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
title_full_unstemmed Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
title_sort application of pso to develop a powerful equation for prediction of flyrock due to blasting
publisher Springer-Verlag London Ltd
publishDate 2016
url http://eprints.utm.my/id/eprint/72821/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975166243&doi=10.1007%2fs00521-016-2434-1&partnerID=40&md5=a9106daf2883f913f46f76a56dedb0ff
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score 13.250246

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