Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures
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=S1516-14392012000600007 |
Resumo: | The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches. |
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Materials research (São Carlos. Online) |
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Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixturesMarshall designstatic creep testbitumen modificationpolypropylene fibersstrain accumulationartificial neural networksclosed form solutionsThe testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.ABM, ABC, ABPol2012-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000600007Materials Research v.15 n.6 2012reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/S1516-14392012005000117info:eu-repo/semantics/openAccessTapkın,SerkanÇevik,AbdulkadirÖzcan,Şenoleng2012-11-20T00:00:00Zoai:scielo:S1516-14392012000600007Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2012-11-20T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
title |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
spellingShingle |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures Tapkın,Serkan Marshall design static creep test bitumen modification polypropylene fibers strain accumulation artificial neural networks closed form solutions |
title_short |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
title_full |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
title_fullStr |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
title_full_unstemmed |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
title_sort |
Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures |
author |
Tapkın,Serkan |
author_facet |
Tapkın,Serkan Çevik,Abdulkadir Özcan,Şenol |
author_role |
author |
author2 |
Çevik,Abdulkadir Özcan,Şenol |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Tapkın,Serkan Çevik,Abdulkadir Özcan,Şenol |
dc.subject.por.fl_str_mv |
Marshall design static creep test bitumen modification polypropylene fibers strain accumulation artificial neural networks closed form solutions |
topic |
Marshall design static creep test bitumen modification polypropylene fibers strain accumulation artificial neural networks closed form solutions |
description |
The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-12-01 |
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=S1516-14392012000600007 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000600007 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1516-14392012005000117 |
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.6 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_ |
1754212661654978560 |