The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network

Shrinkage and creep are the main concrete volume changes over time. This unacceptable concrete deformation leads to stress and cracks creation where eventually reduces the service life of concrete structures. According to this, the prediction of shrinkage and creep strain in concrete structures with...

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Autores principales:	Hosein Garoosiha, Jamal Ahmadi, Hossein Bayat
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	shrinkage strains prediction models artificial neural network training algorithm modern concrete Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/9b70cd49752142aa8fa7464af715c035
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spelling	oai:doaj.org-article:9b70cd49752142aa8fa7464af715c0352021-11-04T15:51:55ZThe assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network2331-191610.1080/23311916.2019.1609179https://doaj.org/article/9b70cd49752142aa8fa7464af715c0352019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1609179https://doaj.org/toc/2331-1916Shrinkage and creep are the main concrete volume changes over time. This unacceptable concrete deformation leads to stress and cracks creation where eventually reduces the service life of concrete structures. According to this, the prediction of shrinkage and creep strain in concrete structures with acceptable accuracy is the significance essential. The extensive investigation accomplished by several researchers has created different relationships and models for forecasting of shrinkage and creep strain based on experimental and analytical observation. Despite effective efforts in this regard, existing models do not have sufficient accuracy for anticipate of shrinkage strain. According to this, in this research, it has been attempted to provide a shrinkage predicting model based on the artificial neural network technique with the application of RILEM database. Also, it has been tried to determine the accuracy of the proposed model in comparison to the existing standard models by statistical analysis. According to the obtained results, by application of the neural network technique, the shrinkage strain could be predicted with acceptable accuracy especial in the extended period.Hosein GaroosihaJamal AhmadiHossein BayatTaylor & Francis Grouparticleshrinkage strainsprediction modelsartificial neural networktraining algorithmmodern concreteEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	shrinkage strains prediction models artificial neural network training algorithm modern concrete Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	shrinkage strains prediction models artificial neural network training algorithm modern concrete Engineering (General). Civil engineering (General) TA1-2040 Hosein Garoosiha Jamal Ahmadi Hossein Bayat The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
description	Shrinkage and creep are the main concrete volume changes over time. This unacceptable concrete deformation leads to stress and cracks creation where eventually reduces the service life of concrete structures. According to this, the prediction of shrinkage and creep strain in concrete structures with acceptable accuracy is the significance essential. The extensive investigation accomplished by several researchers has created different relationships and models for forecasting of shrinkage and creep strain based on experimental and analytical observation. Despite effective efforts in this regard, existing models do not have sufficient accuracy for anticipate of shrinkage strain. According to this, in this research, it has been attempted to provide a shrinkage predicting model based on the artificial neural network technique with the application of RILEM database. Also, it has been tried to determine the accuracy of the proposed model in comparison to the existing standard models by statistical analysis. According to the obtained results, by application of the neural network technique, the shrinkage strain could be predicted with acceptable accuracy especial in the extended period.
format	article
author	Hosein Garoosiha Jamal Ahmadi Hossein Bayat
author_facet	Hosein Garoosiha Jamal Ahmadi Hossein Bayat
author_sort	Hosein Garoosiha
title	The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
title_short	The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
title_full	The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
title_fullStr	The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
title_full_unstemmed	The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network
title_sort	assessment of levenberg–marquardt and bayesian framework training algorithm for prediction of concrete shrinkage by the artificial neural network
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/9b70cd49752142aa8fa7464af715c035
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The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network

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