Prediction of tire-pavement noise of porous asphalt mixture based on mixture surface texture level and distributions

Abstract

An accurate tire-pavement noise prediction model is highly needed by transportation agencies and pavement designers during the porous asphalt mixture design to reduce the tire-pavement noise which has been recognized as a dominant contributor to the overall traffic noise. In this paper, the surface texture level and distributions of porous asphalt mixture are acquired by a recently developed program named as 2-Dimensional Image Texture Analysis Method (2D-ITAM). The acoustic absorption coefficient of porous asphalt mixture is calculated using a proposed sound absorption model based on the micro-structure of porous asphalt mixture. Also, a prediction model correlating the tire-pavement noise level with macro-texture and short wavelength of mega-texture of pavement is established using a multivariate non-linear regression analysis. This prediction model is validated through laboratory experiment demonstrating its effectiveness of predicting the tire-pavement noise level. The model is anticipated to serve as an improved tool which could be considered by practitioners in an optimized porous asphalt mixture design incorporating the evaluation of noise produced by asphalt pavement.