Tire pavement interaction noise of concrete pavements.

Abstract

Vehicles generate noise through their power-train, aerodynamics, exhausts systems and tire pavement interaction. Of these sources, tire pavement interaction is by far the most dominant source at regular cruising highway speeds. A standard means of mitigating the environmental hazards of freeway traffic noise is through the use of noise abatement walls. Such infrastructure, however, can be very expensive and difficult to maintain. Hence the objective of this thesis is to investigate the possibility of reducing traffic noise associated with tire pavement interactions through pavement surface modification. This work was focused on an investigation of pavement surfaces to determine what texture variables affect pavement noise and examine ways of modifying these to improve pavement quietness. The first step was the identification and physical conceptualization of possibly significant noise inducing variables with an emphasis on pavement surface texture. This resulted in a hypothesized model-form for predicting of noise related to tire pavement interactions. It was followed by a large scale field campaign that performed numerous on-board sound intensity (OBSI) measurements on various texture types under various atmospheric conditions. These measurements along with the proposed modelform were then used in an unforced stepwise regression process. It was ascertained that asperity interval (a measure of texture wavelength), texture direction relative to the traffic direction, and texture spikiness (a measure of the probability density function of the texture amplitude) were the major surface finish contributors to tire pavement noise. Contrary to previously held belief, however, the profile depth was not identified as a significant surface finish texture variable. This analysis also identified air temperature and pavement ride quality (measured through the international roughness index IRI) as the significant non-textured contributors to tire pavement interaction noise. The complete regression analysis resulted in a model for predicting OBSI from measurable pavement surface variables, air temperature and ride quality. This model was able to reproduce over 90% of the field measurements to within 1.5 dBA which is the band of the typical human noise detection. It was consequently used to determine the optimum surface texture for a quiet pavement. In addition, the model was used to predict the OBSI for the design of two large scale pavement rehabilitation projects. Moreover, the design pavement texture resulted in a post-construction noise level drop of approximately 5 dBA. The predicted OBSI pre-construction and post-construction were within 1 dBA of the field measurements.