Skip to main content
padlock icon - secure page this page is secure

Statistical model of tyre-road noise for thin layer surfacing

Buy Article:

$17.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: 11.7.1; 75.1

Document Type: Research Article

Affiliations: Research Institute of Highway Ministry Transport

Publication date: February 1, 2017

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more