Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique

The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazi...

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Main Authors: Khandelwal, M., Armaghani, D. J.
Format: Article
Published: Springer International Publishing 2016
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Online Access:http://eprints.utm.my/id/eprint/72723/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961061024&doi=10.1007%2fs10706-015-9970-9&partnerID=40&md5=0571abc5a88f13096e8bd624b9cc3543
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.727232017-11-27T09:02:12Z http://eprints.utm.my/id/eprint/72723/ Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique Khandelwal, M. Armaghani, D. J. TA Engineering (General). Civil engineering (General) The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. Springer International Publishing 2016 Article PeerReviewed Khandelwal, M. and Armaghani, D. J. (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotechnical and Geological Engineering, 34 (2). pp. 605-620. ISSN 0960-3182 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961061024&doi=10.1007%2fs10706-015-9970-9&partnerID=40&md5=0571abc5a88f13096e8bd624b9cc3543
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.
Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
description The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
format Article
author Khandelwal, M.
Armaghani, D. J.
author_facet Khandelwal, M.
Armaghani, D. J.
author_sort Khandelwal, M.
title Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
title_short Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
title_full Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
title_fullStr Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
title_full_unstemmed Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
title_sort prediction of drillability of rocks with strength properties using a hybrid ga-ann technique
publisher Springer International Publishing
publishDate 2016
url http://eprints.utm.my/id/eprint/72723/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961061024&doi=10.1007%2fs10706-015-9970-9&partnerID=40&md5=0571abc5a88f13096e8bd624b9cc3543
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