Tire pavement interaction noise of concrete pavements

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. Traditional mitigation of freeway traffic noise with noise abatement walls is expensive, maintenance -intensive, and in certain environmental conditions counterproductive. Hence this thesis investigates the possibility of reducing traffic noise associated with tire pavement interactions through pavement surface modification.;This work therefore mainly investigated pavement surfaces to determine related texture variables and examined their modification 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 the hypothesis of a model-form for predicting of noise related to tire pavement interactions. It was followed by a large scale field measurement of on-board sound intensity (OBSI) of various texture types under various atmospheric conditions. These measurements along with the proposed model-form were then used in an unforced stepwise regression process. At the 95 percent confidence level 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 texture contributors to tire pavement noise.;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 identified significant variables. Texture depth was not a significant variable.;This model reproduced over 90% of the field measurements to within +/-1.5 dBA (the band of the typical human detection) and determined optimum surface texture for a quiet pavement. Additionally, the model predicted the OBSI for the design of two large scale pavement rehabilitation projects resulting in a post-construction noise level drop of approximately 5 decibels in the "A"-weighted scale (dBA). The projects field-validated the model, by predicting pre-construction OBSI and post-construction OBSI within 1 dBA of the field measurements.