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Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling

In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geot...

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Main Authors: Chen, Wusi, Khandelwal, Manoj, Murlidhar, Bhatawdekar Ramesh, Dieu, Tien Bui, Tahir, M. M., Katebi, Javad
Format: Article
Published: Springer-Verlag London Ltd 2020
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/89825/
http://dx.doi.org/10.1007/s00366-019-00731-2
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spelling my.utm.898252021-03-04T02:46:07Z http://eprints.utm.my/id/eprint/89825/ Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling Chen, Wusi Khandelwal, Manoj Murlidhar, Bhatawdekar Ramesh Dieu, Tien Bui Tahir, M. M. Katebi, Javad TA Engineering (General). Civil engineering (General) In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. Springer-Verlag London Ltd 2020-04-01 Article PeerReviewed Chen, Wusi and Khandelwal, Manoj and Murlidhar, Bhatawdekar Ramesh and Dieu, Tien Bui and Tahir, M. M. and Katebi, Javad (2020) Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling. Engineering with Computers, 36 (2). pp. 783-793. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00731-2 DOI:10.1007/s00366-019-00731-2
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)
Chen, Wusi
Khandelwal, Manoj
Murlidhar, Bhatawdekar Ramesh
Dieu, Tien Bui
Tahir, M. M.
Katebi, Javad
Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
description In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters.
format Article
author Chen, Wusi
Khandelwal, Manoj
Murlidhar, Bhatawdekar Ramesh
Dieu, Tien Bui
Tahir, M. M.
Katebi, Javad
author_facet Chen, Wusi
Khandelwal, Manoj
Murlidhar, Bhatawdekar Ramesh
Dieu, Tien Bui
Tahir, M. M.
Katebi, Javad
author_sort Chen, Wusi
title Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
title_short Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
title_full Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
title_fullStr Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
title_full_unstemmed Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
title_sort assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling
publisher Springer-Verlag London Ltd
publishDate 2020
url http://eprints.utm.my/id/eprint/89825/
http://dx.doi.org/10.1007/s00366-019-00731-2
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score 13.211869

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