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Genetic programming and gene expression programming for flyrock assessment due to mine blasting

This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyro...

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Main Authors: Faradonbeh, R. S., Armaghani, D. J., Monjezi, M., Mohamad, E. T.
Format: Article
Published: Elsevier Ltd 2016
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/72064/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982845293&doi=10.1016%2fj.ijrmms.2016.07.028&partnerID=40&md5=e2deba1ef1af123692ef645603a097c7
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spelling my.utm.720642017-11-23T01:37:07Z http://eprints.utm.my/id/eprint/72064/ Genetic programming and gene expression programming for flyrock assessment due to mine blasting Faradonbeh, R. S. Armaghani, D. J. Monjezi, M. Mohamad, E. T. TA Engineering (General). Civil engineering (General) This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model. Elsevier Ltd 2016 Article PeerReviewed Faradonbeh, R. S. and Armaghani, D. J. and Monjezi, M. and Mohamad, E. T. (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. International Journal of Rock Mechanics and Mining Sciences, 88 . pp. 254-264. ISSN 1365-1609 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982845293&doi=10.1016%2fj.ijrmms.2016.07.028&partnerID=40&md5=e2deba1ef1af123692ef645603a097c7
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)
Faradonbeh, R. S.
Armaghani, D. J.
Monjezi, M.
Mohamad, E. T.
Genetic programming and gene expression programming for flyrock assessment due to mine blasting
description This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model.
format Article
author Faradonbeh, R. S.
Armaghani, D. J.
Monjezi, M.
Mohamad, E. T.
author_facet Faradonbeh, R. S.
Armaghani, D. J.
Monjezi, M.
Mohamad, E. T.
author_sort Faradonbeh, R. S.
title Genetic programming and gene expression programming for flyrock assessment due to mine blasting
title_short Genetic programming and gene expression programming for flyrock assessment due to mine blasting
title_full Genetic programming and gene expression programming for flyrock assessment due to mine blasting
title_fullStr Genetic programming and gene expression programming for flyrock assessment due to mine blasting
title_full_unstemmed Genetic programming and gene expression programming for flyrock assessment due to mine blasting
title_sort genetic programming and gene expression programming for flyrock assessment due to mine blasting
publisher Elsevier Ltd
publishDate 2016
url http://eprints.utm.my/id/eprint/72064/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982845293&doi=10.1016%2fj.ijrmms.2016.07.028&partnerID=40&md5=e2deba1ef1af123692ef645603a097c7
_version_ 1643656348435480576
score 13.211869

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