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

TitleModelling tyre-road noise with data mining techniques
Author(s)Freitas, Elisabete F.
Tinoco, Joaquim Agostinho Barbosa
Soares, Francisco
Costa, Jocilene Otilia da
Cortez, Paulo
Pereira, Paulo A. A.
KeywordsTyre-road noise
Data mining
Model
Texture
Damping
Surface characteristics
Issue date2015
PublisherWalter de Gruyter GmbH
JournalArchives of Acoustics
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.
Abstract(s)The 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.
TypeArticle
URIhttp://hdl.handle.net/1822/39850
DOI10.1515/aoa-2015-0054
ISSN0137-5075
Peer-Reviewedyes
AccessOpen access
Appears in Collections:C-TAC - Artigos em Revistas Internacionais
ISISE - Artigos em Revistas Internacionais

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