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Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based metho...

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Main Authors:	Fang, Q., Bejarbaneh, B. Y., Vatandoust, M., Armaghani, D. J., Murlidhar, B.R., Mohamad, E. T.
Format:	Article
Published:	Springer Science and Business Media Deutschland GmbH 2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/94045/ http://dx.doi.org/10.1007/s00366-019-00872-4
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spelling	my.utm.940452022-02-28T13:17:03Z http://eprints.utm.my/id/eprint/94045/ Strength evaluation of granite block samples with different predictive models Fang, Q. Bejarbaneh, B. Y. Vatandoust, M. Armaghani, D. J. Murlidhar, B.R. Mohamad, E. T. TA Engineering (General). Civil engineering (General) Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Fang, Q. and Bejarbaneh, B. Y. and Vatandoust, M. and Armaghani, D. J. and Murlidhar, B.R. and Mohamad, E. T. (2021) Strength evaluation of granite block samples with different predictive models. Engineering with Computers, 37 (2). pp. 891-908. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00872-4 DOI: 10.1007/s00366-019-00872-4
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) Fang, Q. Bejarbaneh, B. Y. Vatandoust, M. Armaghani, D. J. Murlidhar, B.R. Mohamad, E. T. Strength evaluation of granite block samples with different predictive models
description	Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.
format	Article
author	Fang, Q. Bejarbaneh, B. Y. Vatandoust, M. Armaghani, D. J. Murlidhar, B.R. Mohamad, E. T.
author_facet	Fang, Q. Bejarbaneh, B. Y. Vatandoust, M. Armaghani, D. J. Murlidhar, B.R. Mohamad, E. T.
author_sort	Fang, Q.
title	Strength evaluation of granite block samples with different predictive models
title_short	Strength evaluation of granite block samples with different predictive models
title_full	Strength evaluation of granite block samples with different predictive models
title_fullStr	Strength evaluation of granite block samples with different predictive models
title_full_unstemmed	Strength evaluation of granite block samples with different predictive models
title_sort	strength evaluation of granite block samples with different predictive models
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2021
url	http://eprints.utm.my/id/eprint/94045/ http://dx.doi.org/10.1007/s00366-019-00872-4
_version_	1726791472819208192
score	13.211869

Strength evaluation of granite block samples with different predictive models

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