Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method

In the present paper break-off test as a partially-destructive method is used for durability evaluation of steel fiber reinforced concrete. In recent years, utilizations of steel fibers have been known as an appropriate solution method for sudden fracture of concrete. In order to provide a comprehen...

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Autores principales:	S. Hosein Ghasemzadeh mosavinejad, benyamin ganjeh khosravi, javad razzaghi
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2019
Materias:	break-off test concrete strength steel fiber partially-destructive test artificial neural network Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/11ea550f50b54ccdb7987cdba8bd3ecc
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spelling	oai:doaj.org-article:11ea550f50b54ccdb7987cdba8bd3ecc2021-11-08T15:50:45ZEvaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method2476-39772538-261610.22065/jsce.2017.86365.1194https://doaj.org/article/11ea550f50b54ccdb7987cdba8bd3ecc2019-02-01T00:00:00Zhttps://www.jsce.ir/article_87084_3e05dd3fd51d837d998dd8d10ddd9a5e.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616In the present paper break-off test as a partially-destructive method is used for durability evaluation of steel fiber reinforced concrete. In recent years, utilizations of steel fibers have been known as an appropriate solution method for sudden fracture of concrete. In order to provide a comprehensive statistical database, 24 mixtures are designed with various cement content (400, 450, and 500 Kg/m3), maximum aggregate size (12.5, 25 mm), steel fibre volume fractions (0, 0.33, 0.67, 1 %), and the constant water/cement ratio of 0.4 for all mixtures. Hence, influencing factors of steel fiber reinforced concrete characteristics and break-off test results are evaluated. The investigations show that the volume fraction of steel fibers and its features significantly affect the results of break-off test. Furthermore, in this study conventional numerical neural networks are developed for predicting the compressive strength of concrete with various mixtures and ages. ANN is sophisticatedly capable of being trained from the existent data and extending their behavior on a new dataset. This ability introduced ANN as an apt tool for modeling the complex mechanisms and systems in engineering applications. Statistical indices are used to compare the efficiency and accuracy of models. The result of this study has confirmed the accuracy of artificial neural network models in determination of the compressive strength of concrete.S. Hosein Ghasemzadeh mosavinejadbenyamin ganjeh khosravijavad razzaghiIranian Society of Structrual Engineering (ISSE)articlebreak-off testconcrete strengthsteel fiberpartially-destructive testartificial neural networkBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 5, Iss 4, Pp 41-56 (2019)
institution	DOAJ
collection	DOAJ
language	FA
topic	break-off test concrete strength steel fiber partially-destructive test artificial neural network Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	break-off test concrete strength steel fiber partially-destructive test artificial neural network Bridge engineering TG1-470 Building construction TH1-9745 S. Hosein Ghasemzadeh mosavinejad benyamin ganjeh khosravi javad razzaghi Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
description	In the present paper break-off test as a partially-destructive method is used for durability evaluation of steel fiber reinforced concrete. In recent years, utilizations of steel fibers have been known as an appropriate solution method for sudden fracture of concrete. In order to provide a comprehensive statistical database, 24 mixtures are designed with various cement content (400, 450, and 500 Kg/m3), maximum aggregate size (12.5, 25 mm), steel fibre volume fractions (0, 0.33, 0.67, 1 %), and the constant water/cement ratio of 0.4 for all mixtures. Hence, influencing factors of steel fiber reinforced concrete characteristics and break-off test results are evaluated. The investigations show that the volume fraction of steel fibers and its features significantly affect the results of break-off test. Furthermore, in this study conventional numerical neural networks are developed for predicting the compressive strength of concrete with various mixtures and ages. ANN is sophisticatedly capable of being trained from the existent data and extending their behavior on a new dataset. This ability introduced ANN as an apt tool for modeling the complex mechanisms and systems in engineering applications. Statistical indices are used to compare the efficiency and accuracy of models. The result of this study has confirmed the accuracy of artificial neural network models in determination of the compressive strength of concrete.
format	article
author	S. Hosein Ghasemzadeh mosavinejad benyamin ganjeh khosravi javad razzaghi
author_facet	S. Hosein Ghasemzadeh mosavinejad benyamin ganjeh khosravi javad razzaghi
author_sort	S. Hosein Ghasemzadeh mosavinejad
title	Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
title_short	Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
title_full	Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
title_fullStr	Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
title_full_unstemmed	Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
title_sort	evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2019
url	https://doaj.org/article/11ea550f50b54ccdb7987cdba8bd3ecc
work_keys_str_mv	AT shoseinghasemzadehmosavinejad evaluationofneuralnetworkmodelforcompressivestrengthofthesteelfiberreinforcedconcreteusingbreakoffmethod AT benyaminganjehkhosravi evaluationofneuralnetworkmodelforcompressivestrengthofthesteelfiberreinforcedconcreteusingbreakoffmethod AT javadrazzaghi evaluationofneuralnetworkmodelforcompressivestrengthofthesteelfiberreinforcedconcreteusingbreakoffmethod
_version_	1718441636951752704

Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method

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