Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Latin American journal of solids and structures (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014001100002 |
Resumo: | In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength. |
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Latin American journal of solids and structures (Online) |
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Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural networkFiberSelf-compacting concreteNano-silicamechanical propertiesartificial neural networkIn this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength.Associação Brasileira de Ciências Mecânicas2014-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014001100002Latin American Journal of Solids and Structures v.11 n.11 2014reponame:Latin American journal of solids and structures (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/S1679-78252014001100002info:eu-repo/semantics/openAccessTavakoli,Hamid RezaOmran,Omid LotfiShiade,Masoud FalahtabarKutanaei,Saman Soleimanieng2014-12-08T00:00:00Zoai:scielo:S1679-78252014001100002Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=1679-7825&lng=pt&nrm=isohttps://old.scielo.br/oai/scielo-oai.phpabcm@abcm.org.br||maralves@usp.br1679-78251679-7817opendoar:2014-12-08T00:00Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false |
dc.title.none.fl_str_mv |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
title |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
spellingShingle |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network Tavakoli,Hamid Reza Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
title_short |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
title_full |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
title_fullStr |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
title_full_unstemmed |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
title_sort |
Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network |
author |
Tavakoli,Hamid Reza |
author_facet |
Tavakoli,Hamid Reza Omran,Omid Lotfi Shiade,Masoud Falahtabar Kutanaei,Saman Soleimani |
author_role |
author |
author2 |
Omran,Omid Lotfi Shiade,Masoud Falahtabar Kutanaei,Saman Soleimani |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Tavakoli,Hamid Reza Omran,Omid Lotfi Shiade,Masoud Falahtabar Kutanaei,Saman Soleimani |
dc.subject.por.fl_str_mv |
Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
topic |
Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
description |
In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 |
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=S1679-78252014001100002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014001100002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1679-78252014001100002 |
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 |
Associação Brasileira de Ciências Mecânicas |
publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
dc.source.none.fl_str_mv |
Latin American Journal of Solids and Structures v.11 n.11 2014 reponame:Latin American journal of solids and structures (Online) instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
instname_str |
Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
instacron_str |
ABCM |
institution |
ABCM |
reponame_str |
Latin American journal of solids and structures (Online) |
collection |
Latin American journal of solids and structures (Online) |
repository.name.fl_str_mv |
Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
repository.mail.fl_str_mv |
abcm@abcm.org.br||maralves@usp.br |
_version_ |
1754302887673987072 |