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Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests suc...

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Main Authors: Armaghani, Danial Jahed, Mohamad, Edy Tonnizam, Hajihassani, Mohsen, Yagiz, Saffet, Motaghedi, Hossein
Format: Article
Published: 2016
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/68865/
https://link.springer.com/article/10.1007/s00366-015-0410-5
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spelling my.utm.688652017-11-20T08:52:11Z http://eprints.utm.my/id/eprint/68865/ Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances Armaghani, Danial Jahed Mohamad, Edy Tonnizam Hajihassani, Mohsen Yagiz, Saffet Motaghedi, Hossein TA Engineering (General). Civil engineering (General) Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (Is(50)), Schmidt hammer (Rn) and p-wave velocity (Vp) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like Rn, p-wave and Is(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R2), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R2 equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types. 2016 Article PeerReviewed Armaghani, Danial Jahed and Mohamad, Edy Tonnizam and Hajihassani, Mohsen and Yagiz, Saffet and Motaghedi, Hossein (2016) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computers, 32 (2). pp. 189-206. https://link.springer.com/article/10.1007/s00366-015-0410-5
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)
Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
Hajihassani, Mohsen
Yagiz, Saffet
Motaghedi, Hossein
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
description Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (Is(50)), Schmidt hammer (Rn) and p-wave velocity (Vp) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like Rn, p-wave and Is(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R2), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R2 equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.
format Article
author Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
Hajihassani, Mohsen
Yagiz, Saffet
Motaghedi, Hossein
author_facet Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
Hajihassani, Mohsen
Yagiz, Saffet
Motaghedi, Hossein
author_sort Armaghani, Danial Jahed
title Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
title_short Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
title_full Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
title_fullStr Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
title_full_unstemmed Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
title_sort application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
publishDate 2016
url http://eprints.utm.my/id/eprint/68865/
https://link.springer.com/article/10.1007/s00366-015-0410-5
_version_ 1643655986080120832
score 13.211869

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