Exploring the feasibility of evaluating asphalt pavement surface macro-texture using image-based texture analysis method

Pavement surface texture significantly affects tyre–pavement friction and noise characteristics. The traditional methods for evaluating pavement surface texture result in a single index called mean profile depth (MPD). Although this index can reflect the overall texture properties, it cannot reveal the range and distribution of pavement surface texture, which play a critical role in prediction of tyre–pavement interaction characteristics. In this paper, a cost-effective and relatively precise image-based texture analysis method (ITAM) was developed based on digital image processing and spectral analysis technologies. Mixture sample produced using surperpave gyratory compactor (SGC) is cut into three sections which are scanned using a standard commercial scanner. Mixture surface profile is then identified from the scanned cut section images by applying a series of image analysis technics. Afterwards, a discrete Fourier transform is applied on the mixture surface profiles to calculate the texture distribution indicators through the ITAM software. Additionally, the traditional texture indicator (MPD) is derived. Previous researchers have shown that the stationary laser profilometer (SLP) serves as an effective method to characterise pavement texture properties as this method correlates well with traditional texture testing methods. In this study, the ITAM analysis results are verified by comparing with those from the SLP method. It is shown that ITAM results correlate well with SLP and therefore considered as an effective method to characterise pavement surface texture properties. The results indicate that this method is a promising and powerful tool for future application in mixture designs to estimate texture as related to noise and friction.