Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network

Creep is a fundamental time-dependent property of rock. As one of the main surrounding rocks of underground engineering, layered siltstone is governed by creep to a great extent because of special structure. Based on the structural characteristics of layered siltstone, a viscoelastic–viscoplastic mo...

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Autores principales:	Yang Yiran, Lai Xingping, Luo Tao, Yuan Kekuo, Cui Feng
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	creep model layered siltstone numerical simulation creep test rbf neural network Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/eeeeeef172fd46519e142983af6b3907
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spelling	oai:doaj.org-article:eeeeeef172fd46519e142983af6b39072021-12-05T14:10:48ZStudy on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network2391-544710.1515/geo-2020-0224https://doaj.org/article/eeeeeef172fd46519e142983af6b39072021-01-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0224https://doaj.org/toc/2391-5447Creep is a fundamental time-dependent property of rock. As one of the main surrounding rocks of underground engineering, layered siltstone is governed by creep to a great extent because of special structure. Based on the structural characteristics of layered siltstone, a viscoelastic–viscoplastic model was proposed to simulate and present its creep property. To verify the accuracy of the model, governing equation of the viscoelastic–viscoplastic model was introduced into finite element difference program to simulate a series of creep tests of layered siltstone. Meanwhile, creep tests on layered siltstone were conducted. Numerical simulation results of the viscoelastic–viscoplastic model were compared with creep test data. Mean relative error of creep test data and numerical simulation result was 0.41%. Combined with Lyapunov function, the radial basis function (RBF) neural network trained with creep test data was adopted. Mean relative error of creep test data and RBF neural network data was 0.57%. The results further showed high accuracy and stability of RBF neural network and the viscoelastic–viscoplastic model.Yang YiranLai XingpingLuo TaoYuan KekuoCui FengDe Gruyterarticlecreep modellayered siltstonenumerical simulationcreep testrbf neural networkGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 72-84 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	creep model layered siltstone numerical simulation creep test rbf neural network Geology QE1-996.5
spellingShingle	creep model layered siltstone numerical simulation creep test rbf neural network Geology QE1-996.5 Yang Yiran Lai Xingping Luo Tao Yuan Kekuo Cui Feng Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
description	Creep is a fundamental time-dependent property of rock. As one of the main surrounding rocks of underground engineering, layered siltstone is governed by creep to a great extent because of special structure. Based on the structural characteristics of layered siltstone, a viscoelastic–viscoplastic model was proposed to simulate and present its creep property. To verify the accuracy of the model, governing equation of the viscoelastic–viscoplastic model was introduced into finite element difference program to simulate a series of creep tests of layered siltstone. Meanwhile, creep tests on layered siltstone were conducted. Numerical simulation results of the viscoelastic–viscoplastic model were compared with creep test data. Mean relative error of creep test data and numerical simulation result was 0.41%. Combined with Lyapunov function, the radial basis function (RBF) neural network trained with creep test data was adopted. Mean relative error of creep test data and RBF neural network data was 0.57%. The results further showed high accuracy and stability of RBF neural network and the viscoelastic–viscoplastic model.
format	article
author	Yang Yiran Lai Xingping Luo Tao Yuan Kekuo Cui Feng
author_facet	Yang Yiran Lai Xingping Luo Tao Yuan Kekuo Cui Feng
author_sort	Yang Yiran
title	Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
title_short	Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
title_full	Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
title_fullStr	Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
title_full_unstemmed	Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
title_sort	study on the viscoelastic–viscoplastic model of layered siltstone using creep test and rbf neural network
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/eeeeeef172fd46519e142983af6b3907
work_keys_str_mv	AT yangyiran studyontheviscoelasticviscoplasticmodeloflayeredsiltstoneusingcreeptestandrbfneuralnetwork AT laixingping studyontheviscoelasticviscoplasticmodeloflayeredsiltstoneusingcreeptestandrbfneuralnetwork AT luotao studyontheviscoelasticviscoplasticmodeloflayeredsiltstoneusingcreeptestandrbfneuralnetwork AT yuankekuo studyontheviscoelasticviscoplasticmodeloflayeredsiltstoneusingcreeptestandrbfneuralnetwork AT cuifeng studyontheviscoelasticviscoplasticmodeloflayeredsiltstoneusingcreeptestandrbfneuralnetwork
_version_	1718371752779710464

Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network

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