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Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

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dc.contributor.authorTapkın, Serkan
dc.contributor.authorÖzcan, Şenol
dc.contributor.authorŞenol Özcan
dc.contributor.orcid0000-0003-1417-9972
dc.contributor.orcid0000-0002-5253-2952
dc.date.accessioned2025-11-13T10:34:25Z
dc.date.issued2012-09-25
dc.identifier.doihttps://doi.org/10.1590/s1516-14392012005000117
dc.identifier.endpage883
dc.identifier.issn1516-1439
dc.identifier.issue6
dc.identifier.openalexW2104765421
dc.identifier.startpage865
dc.identifier.urihttps://hdl.handle.net/11421/4579
dc.identifier.urihttps://doi.org/10.1590/s1516-14392012005000117
dc.identifier.volume15
dc.language.isoen
dc.relation.ispartofMaterials Research
dc.rightsopenAccess
dc.subjectCreep
dc.subjectAsphalt
dc.subjectArtificial neural network
dc.subjectPolypropylene
dc.subjectStiffness
dc.subjectStability (learning theory)
dc.subjectRut
dc.subjectUniversal testing machine
dc.subjectMaterials science
dc.subjectFlow (mathematics)
dc.subjectComputer science
dc.subjectStructural engineering
dc.subjectMechanical engineering
dc.subjectMechanics
dc.subjectComposite material
dc.subjectEngineering
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectPhysics
dc.titleUtilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5027623678
local.authorid.openalexA5053923143

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