Statistical model of tyre-road noise for thin layer surfacing

Mingliang Li, Wim Van Keulen, Halil Ceylan, Dongwei Cao, Martin Van De Ven, André Molenaar

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Noise produced from the tyre-road surface interface is one of the most important contributions to the overall traffic noise and there is an increasing requirement for predicting the tyre-road noise levels prior to road construction in the Netherlands. In practice, a model with a simple structure as well as a high accuracy is applicable in road engineering. Also, material properties are preferred to be used as input variables of the prediction model, which will facilitate the pavement design. Based on these considerations, models are developed for evaluating the tyre-road noise from the asphalt mixture compositions and road surface characteristics. They are statistical models developed from the measurements on thin layer surfacings in the Netherlands. Different regression methods, model types and input variable combinations are taken into account. The selection of the model is due to the fitness of the prediction and validation by using measurement data from in service road sections. Two models, which evaluate the tyre-road noise level from the surface characteristics and from material properties, respectively, are finally selected. By using these models, only a small number of input variables are required and reliable predictions can be provided. The models achieved in this study can be used for predicting the tyre-road noise generation in road engineering and investigating the influence of surface characteristics and material properties on tyre- road noise levels.

Original languageEnglish
Pages (from-to)22-32
Number of pages11
JournalNoise Control Engineering Journal
Volume65
Issue number1
DOIs
Publication statusPublished - 1 Feb 2017

Fingerprint

Dive into the research topics of 'Statistical model of tyre-road noise for thin layer surfacing'. Together they form a unique fingerprint.

Cite this