Artificial neural networks application to predict bond steel-concrete in pull-out tests
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista IBRACON de Estruturas e Materiais |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952017000501051 |
Resumo: | Abstract This study aims the possibility of using the pull-out test results - bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests. |
id |
IBRACON-1_decd77030b156e30bc2bc0d72a6d92f6 |
---|---|
oai_identifier_str |
oai:scielo:S1983-41952017000501051 |
network_acronym_str |
IBRACON-1 |
network_name_str |
Revista IBRACON de Estruturas e Materiais |
repository_id_str |
|
spelling |
Artificial neural networks application to predict bond steel-concrete in pull-out testsbond steel-concreteartificial neural networkspull-out testconcrete strengthAPULOT testAbstract This study aims the possibility of using the pull-out test results - bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.IBRACON - Instituto Brasileiro do Concreto2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952017000501051Revista IBRACON de Estruturas e Materiais v.10 n.5 2017reponame:Revista IBRACON de Estruturas e Materiaisinstname:Instituto Brasileiro do Concreto (IBRACON)instacron:IBRACON10.1590/s1983-41952017000500007info:eu-repo/semantics/openAccessLORENZI,A.SILVA,B. V.BARBOSA,M. P.SILVA FILHO,L. C. P.eng2017-11-07T00:00:00Zoai:scielo:S1983-41952017000501051Revistahttp://www.revistas.ibracon.org.br/index.php/riemhttps://old.scielo.br/oai/scielo-oai.phpeditores.riem@gmail.com||arlene@ibracon.org.br1983-41951983-4195opendoar:2017-11-07T00:00Revista IBRACON de Estruturas e Materiais - Instituto Brasileiro do Concreto (IBRACON)false |
dc.title.none.fl_str_mv |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
title |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
spellingShingle |
Artificial neural networks application to predict bond steel-concrete in pull-out tests LORENZI,A. bond steel-concrete artificial neural networks pull-out test concrete strength APULOT test |
title_short |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
title_full |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
title_fullStr |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
title_full_unstemmed |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
title_sort |
Artificial neural networks application to predict bond steel-concrete in pull-out tests |
author |
LORENZI,A. |
author_facet |
LORENZI,A. SILVA,B. V. BARBOSA,M. P. SILVA FILHO,L. C. P. |
author_role |
author |
author2 |
SILVA,B. V. BARBOSA,M. P. SILVA FILHO,L. C. P. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
LORENZI,A. SILVA,B. V. BARBOSA,M. P. SILVA FILHO,L. C. P. |
dc.subject.por.fl_str_mv |
bond steel-concrete artificial neural networks pull-out test concrete strength APULOT test |
topic |
bond steel-concrete artificial neural networks pull-out test concrete strength APULOT test |
description |
Abstract This study aims the possibility of using the pull-out test results - bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-01 |
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=S1983-41952017000501051 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952017000501051 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/s1983-41952017000500007 |
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 |
IBRACON - Instituto Brasileiro do Concreto |
publisher.none.fl_str_mv |
IBRACON - Instituto Brasileiro do Concreto |
dc.source.none.fl_str_mv |
Revista IBRACON de Estruturas e Materiais v.10 n.5 2017 reponame:Revista IBRACON de Estruturas e Materiais instname:Instituto Brasileiro do Concreto (IBRACON) instacron:IBRACON |
instname_str |
Instituto Brasileiro do Concreto (IBRACON) |
instacron_str |
IBRACON |
institution |
IBRACON |
reponame_str |
Revista IBRACON de Estruturas e Materiais |
collection |
Revista IBRACON de Estruturas e Materiais |
repository.name.fl_str_mv |
Revista IBRACON de Estruturas e Materiais - Instituto Brasileiro do Concreto (IBRACON) |
repository.mail.fl_str_mv |
editores.riem@gmail.com||arlene@ibracon.org.br |
_version_ |
1754193605232164864 |