Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

Detalhes bibliográficos
Autor(a) principal: Specht,Luciano Pivoto
Data de Publicação: 2007
Outros Autores: Khatchatourian,Oleg, Brito,Lélio Antônio Teixeira, Ceratti,Jorge Augusto Pereira
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=S1516-14392007000100015
Resumo: It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.
id ABMABCABPOL-1_2d5d970e5dd4acdd764106b36fdc77d6
oai_identifier_str oai:scielo:S1516-14392007000100015
network_acronym_str ABMABCABPOL-1
network_name_str Materials research (São Carlos. Online)
repository_id_str
spelling Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networksasphalt-rubberviscositymodelingartificial neural networkIt is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.ABM, ABC, ABPol2007-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392007000100015Materials Research v.10 n.1 2007reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/S1516-14392007000100015info:eu-repo/semantics/openAccessSpecht,Luciano PivotoKhatchatourian,OlegBrito,Lélio Antônio TeixeiraCeratti,Jorge Augusto Pereiraeng2007-05-03T00:00:00Zoai:scielo:S1516-14392007000100015Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2007-05-03T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
title Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
spellingShingle Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
Specht,Luciano Pivoto
asphalt-rubber
viscosity
modeling
artificial neural network
title_short Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
title_full Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
title_fullStr Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
title_full_unstemmed Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
title_sort Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
author Specht,Luciano Pivoto
author_facet Specht,Luciano Pivoto
Khatchatourian,Oleg
Brito,Lélio Antônio Teixeira
Ceratti,Jorge Augusto Pereira
author_role author
author2 Khatchatourian,Oleg
Brito,Lélio Antônio Teixeira
Ceratti,Jorge Augusto Pereira
author2_role author
author
author
dc.contributor.author.fl_str_mv Specht,Luciano Pivoto
Khatchatourian,Oleg
Brito,Lélio Antônio Teixeira
Ceratti,Jorge Augusto Pereira
dc.subject.por.fl_str_mv asphalt-rubber
viscosity
modeling
artificial neural network
topic asphalt-rubber
viscosity
modeling
artificial neural network
description It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.
publishDate 2007
dc.date.none.fl_str_mv 2007-03-01
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=S1516-14392007000100015
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392007000100015
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1516-14392007000100015
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.10 n.1 2007
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_ 1754212658516590592

Registros relacionados