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TitleTraffic noise and pavement distresses: modelling and assessment of input parameters influence through data mining techniques
Author(s)Freitas, E. F.
Martins, Francisco F.
Oliveira, Ana
Rocha Segundo, Iran
Torres, Hélder
KeywordsTyre-pavement noise
Acoustic and psychoacoustic indicators
Pavement distresses
Data Mining
Support vector machines
Artificial neural networks
Issue dateSep-2018
JournalApplied Acoustics
CitationFreitas E.F., Martins F. F., Oliveira A., Rocha Segundo I., Torres H. Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques, Applied Acoustics, Vol. 138, pp. 147–155, doi:10.1016/j.apacoust.2018.03.019, 2018
Abstract(s)Traffic noise affects greatly health and well-being of people, consequently the knowledge and control of the factors affecting it is very important. In this study models to predict tyre-pavement noise acoustic and psychoacoustic indicators based on type of pavement, texture, pavement distresses and speed were developed and used to assess the importance of each factor. By applying data mining techniques, in particular artificial neural networks and support vector machines, models with good predictive capacity of both acoustic and psychoacoustic noise indicators were obtained, constituting a precious tool to reduce the tyre-pavement noise. Moreover, the proposed models allowed for the assessment of the influence of the input parameters controlling noise such as: type of pavement, texture, speed and pavement distresses for the first time. It was found that pavement distresses and, as expected, speed influence strongly tyre-pavement noise. In this way it is clearly shown that preventive maintenance of road pavements by authorities, which eliminates distresses, can have an important effect on tyre-road noise, promoting the well-being of the populations.
AccessOpen access
Appears in Collections:C-TAC - Artigos em Revistas Internacionais

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