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Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques

Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree m...

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Main Authors: Long, Tsang, Biao, He, A. Rashid, Ahmad Safuan, Jalil, Abduladheem Turki, Sabri, Mohanad Muayad
Format: Article
Language:English
Published: MDPI 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/100982/1/AhmadSafuan2022_PredictingtheYoungsModulusofRockMaterial.pdf
http://eprints.utm.my/id/eprint/100982/
http://dx.doi.org/10.3390/app122010258
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spelling my.utm.1009822023-05-23T10:22:37Z http://eprints.utm.my/id/eprint/100982/ Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques Long, Tsang Biao, He A. Rashid, Ahmad Safuan Jalil, Abduladheem Turki Sabri, Mohanad Muayad TA Engineering (General). Civil engineering (General) Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study, secondly, boosting models outperformed the bagging models. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100982/1/AhmadSafuan2022_PredictingtheYoungsModulusofRockMaterial.pdf Long, Tsang and Biao, He and A. Rashid, Ahmad Safuan and Jalil, Abduladheem Turki and Sabri, Mohanad Muayad (2022) Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques. Applied Sciences (Switzerland), 12 (20). pp. 1-15. ISSN 2076-3417 http://dx.doi.org/10.3390/app122010258 DOI : 10.3390/app122010258
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)
Long, Tsang
Biao, He
A. Rashid, Ahmad Safuan
Jalil, Abduladheem Turki
Sabri, Mohanad Muayad
Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
description Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study, secondly, boosting models outperformed the bagging models.
format Article
author Long, Tsang
Biao, He
A. Rashid, Ahmad Safuan
Jalil, Abduladheem Turki
Sabri, Mohanad Muayad
author_facet Long, Tsang
Biao, He
A. Rashid, Ahmad Safuan
Jalil, Abduladheem Turki
Sabri, Mohanad Muayad
author_sort Long, Tsang
title Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
title_short Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
title_full Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
title_fullStr Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
title_full_unstemmed Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
title_sort predicting the young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
publisher MDPI
publishDate 2022
url http://eprints.utm.my/id/eprint/100982/1/AhmadSafuan2022_PredictingtheYoungsModulusofRockMaterial.pdf
http://eprints.utm.my/id/eprint/100982/
http://dx.doi.org/10.3390/app122010258
_version_ 1768006592791314432
score 13.211869

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