Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Materials research (São Carlos. Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000300016 |
Resumo: | In the present paper, two models based on artificial neural networks (ANN) and gene expression programming (GEP) for predicting splitting tensile strength and water absorption of concretes containing ZnO2 nanoparticles at different ages of curing have been developed. To build these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The used data in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models, the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles were predicted. The training and testing results in these two models have shown the strong potential of the models for predicting the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles. Although neural networks have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural networks. |
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Materials research (São Carlos. Online) |
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Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concreteneural networksgenetic programmingnanoparticlesconcretetensile testwater permeabilityIn the present paper, two models based on artificial neural networks (ANN) and gene expression programming (GEP) for predicting splitting tensile strength and water absorption of concretes containing ZnO2 nanoparticles at different ages of curing have been developed. To build these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The used data in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models, the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles were predicted. The training and testing results in these two models have shown the strong potential of the models for predicting the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles. Although neural networks have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural networks.ABM, ABC, ABPol2012-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000300016Materials Research v.15 n.3 2012reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/S1516-14392012005000057info:eu-repo/semantics/openAccessNazari,AliAzimzadegan,Tohideng2022-09-01T00:00:00Zoai:scielo:S1516-14392012000300016Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2022-09-01T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
title |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
spellingShingle |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete Nazari,Ali neural networks genetic programming nanoparticles concrete tensile test water permeability |
title_short |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
title_full |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
title_fullStr |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
title_full_unstemmed |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
title_sort |
Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete |
author |
Nazari,Ali |
author_facet |
Nazari,Ali Azimzadegan,Tohid |
author_role |
author |
author2 |
Azimzadegan,Tohid |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Nazari,Ali Azimzadegan,Tohid |
dc.subject.por.fl_str_mv |
neural networks genetic programming nanoparticles concrete tensile test water permeability |
topic |
neural networks genetic programming nanoparticles concrete tensile test water permeability |
description |
In the present paper, two models based on artificial neural networks (ANN) and gene expression programming (GEP) for predicting splitting tensile strength and water absorption of concretes containing ZnO2 nanoparticles at different ages of curing have been developed. To build these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The used data in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models, the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles were predicted. The training and testing results in these two models have shown the strong potential of the models for predicting the splitting tensile strength and water absorption values of concretes containing ZnO2 nanoparticles. Although neural networks have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural networks. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000300016 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000300016 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1516-14392012005000057 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
ABM, ABC, ABPol |
publisher.none.fl_str_mv |
ABM, ABC, ABPol |
dc.source.none.fl_str_mv |
Materials Research v.15 n.3 2012 reponame:Materials research (São Carlos. Online) instname:Universidade Federal de São Carlos (UFSCAR) instacron:ABM ABC ABPOL |
instname_str |
Universidade Federal de São Carlos (UFSCAR) |
instacron_str |
ABM ABC ABPOL |
institution |
ABM ABC ABPOL |
reponame_str |
Materials research (São Carlos. Online) |
collection |
Materials research (São Carlos. Online) |
repository.name.fl_str_mv |
Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR) |
repository.mail.fl_str_mv |
dedz@power.ufscar.br |
_version_ |
1754212661320482816 |