Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network

Abstract: This paper presents the modeling of an experimental investigation carried out to evaluate some mechanical and durability properties of concrete mixtures in which cement was partially replaced with Class C and Class F fly ash. A total of 39 mixtures with different mix designs were prepared....

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Autores principales:	Akyuncu,Veysel, Uysal,Mucteba, Tanyildizi,Harun, Sumer,Mansur
Lenguaje:	English
Publicado:	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile 2018
Materias:	Class F fly ash Class C fly ash the weight change the length change sulfate resistance
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000300337
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spelling	oai:scielo:S0718-915X20180003003372019-03-28Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural networkAkyuncu,VeyselUysal,MuctebaTanyildizi,HarunSumer,Mansur Class F fly ash Class C fly ash the weight change the length change sulfate resistance Abstract: This paper presents the modeling of an experimental investigation carried out to evaluate some mechanical and durability properties of concrete mixtures in which cement was partially replaced with Class C and Class F fly ash. A total of 39 mixtures with different mix designs were prepared. After compressive strength testing, the mixtures containing Class F and Class C fly ashes which had similar compressive strength values to control mixtures at 28 d for each series were used for sulfate resistance tests. The degree of sulfate attack was evaluated using expansion and weight loss. The test results indicated that Class C fly ash showed higher compressive strength than Class F fly ash. Moreover, the addition of fly ash significantly increased the resistance to sulfate attack when each amount of fly ash addition regardless of fly ash types was employed. In this paper, the Artificial Neural Network (ANNs) techniques were used to model the relative change in the weight and length of the concrete exposed to sulfate. The best algorithm for length changes of concrete exposed to sulfate is BFGS quasi-Newton backpropagation algorithm while the best algorithm for weight changes of concrete exposed to sulfate is the Levenberg-Marquardt backpropagation algorithm.info:eu-repo/semantics/openAccessEscuela de Construcción Civil, Pontificia Universidad Católica de ChileRevista de la construcción v.17 n.3 20182018-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000300337en10.7764/rdlc.17.3.337
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Class F fly ash Class C fly ash the weight change the length change sulfate resistance
spellingShingle	Class F fly ash Class C fly ash the weight change the length change sulfate resistance Akyuncu,Veysel Uysal,Mucteba Tanyildizi,Harun Sumer,Mansur Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
description	Abstract: This paper presents the modeling of an experimental investigation carried out to evaluate some mechanical and durability properties of concrete mixtures in which cement was partially replaced with Class C and Class F fly ash. A total of 39 mixtures with different mix designs were prepared. After compressive strength testing, the mixtures containing Class F and Class C fly ashes which had similar compressive strength values to control mixtures at 28 d for each series were used for sulfate resistance tests. The degree of sulfate attack was evaluated using expansion and weight loss. The test results indicated that Class C fly ash showed higher compressive strength than Class F fly ash. Moreover, the addition of fly ash significantly increased the resistance to sulfate attack when each amount of fly ash addition regardless of fly ash types was employed. In this paper, the Artificial Neural Network (ANNs) techniques were used to model the relative change in the weight and length of the concrete exposed to sulfate. The best algorithm for length changes of concrete exposed to sulfate is BFGS quasi-Newton backpropagation algorithm while the best algorithm for weight changes of concrete exposed to sulfate is the Levenberg-Marquardt backpropagation algorithm.
author	Akyuncu,Veysel Uysal,Mucteba Tanyildizi,Harun Sumer,Mansur
author_facet	Akyuncu,Veysel Uysal,Mucteba Tanyildizi,Harun Sumer,Mansur
author_sort	Akyuncu,Veysel
title	Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
title_short	Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
title_full	Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
title_fullStr	Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
title_full_unstemmed	Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
title_sort	modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
publisher	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2018000300337
work_keys_str_mv	AT akyuncuveysel modelingtheweightandlengthchangesoftheconcreteexposedtosulfateusingartificialneuralnetwork AT uysalmucteba modelingtheweightandlengthchangesoftheconcreteexposedtosulfateusingartificialneuralnetwork AT tanyildiziharun modelingtheweightandlengthchangesoftheconcreteexposedtosulfateusingartificialneuralnetwork AT sumermansur modelingtheweightandlengthchangesoftheconcreteexposedtosulfateusingartificialneuralnetwork
_version_	1714206292840546304

Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network

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