Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39850

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dc.contributor.authorFreitas, Elisabete F.por
dc.contributor.authorTinoco, Joaquim Agostinho Barbosapor
dc.contributor.authorSoares, Franciscopor
dc.contributor.authorCosta, Jocilene Otilia dapor
dc.contributor.authorCortez, Paulopor
dc.contributor.authorPereira, Paulo A. A.por
dc.date.accessioned2016-02-01T14:10:53Z-
dc.date.issued2015-
dc.identifier.citationElisabete Fraga FREITAS, Joaquim TINOCO, Francisco SOARES, Jocilene COSTA, Paulo CORTEZ, and Paulo PEREIRA. Modelling tyre-road noise with data mining techniques. Archives of Acoustics, 40(4):547{560, 2015. http://dx.doi.org/10.1515/aoa-2015-0054.por
dc.identifier.issn0137-5075por
dc.identifier.urihttp://hdl.handle.net/1822/39850-
dc.description.abstractThe research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.por
dc.description.sponsorshipThis research was financed by FEDER Funds through “Programa Operacional Factores de Competitividade – COMPETE” and by Portuguese Funds through FCT – “Fundação para a Ciência e a Tecnologia”, within the Project PEst-OE/ECI/UI4047/2014.por
dc.language.isoengpor
dc.publisherWalter de Gruyter GmbHpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/135985/PTpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/135985/PT-
dc.rightsopenAccess-
dc.subjectTyre-road noisepor
dc.subjectData miningpor
dc.subjectModelpor
dc.subjectTexturepor
dc.subjectDampingpor
dc.subjectSurface characteristicspor
dc.titleModelling tyre-road noise with data mining techniquespor
dc.typearticle-
dc.peerreviewedyespor
sdum.publicationstatuspublishedpor
oaire.citationStartPage547por
oaire.citationEndPage560por
oaire.citationIssue4por
oaire.citationTitleArchives of Acousticspor
oaire.citationVolume40por
dc.identifier.doi10.1515/aoa-2015-0054por
dc.subject.fosEngenharia e Tecnologia::Engenharia Civilpor
dc.subject.wosScience & Technologypor
sdum.journalArchives of Acousticspor
Appears in Collections:C-TAC - Artigos em Revistas Internacionais
ISISE - Artigos em Revistas Internacionais

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