Evaluating the mastic distribution of asphalt mixtures based on a new thickness threshold using 2D image planers

Abstract

The microstructures of asphalt mastic have been considered as an important micromechanical mixture property related to the macro performance of asphalt mixtures based on the multi-scale analysis methods. In this study, a reasonable two-dimensional image acquisition and processing procedure was utilised to acquire the microstructures of asphalt mastic based on the minimum distance principle. Seven asphalt mixtures with varying gradation types and nominal maximum aggregate sizes (NMAS) were selected. A logarithmic normal distribution model was used to describe the mastic thickness distribution and the uniformity index of mastic (UIM) were derived in aspect of position and orientation. Especially, the differences in mastic distribution between seven selected mixtures were fully quantified. According to the results of this study, with larger NMAS, the asphalt mixture tends to have larger average mastic thickness, wider range of mastic thickness sizes and worse mastic uniformity. With the same NMAS, the skeleton and dense structure gradation could aggravate the concentration of asphalt mastic. New indices, namely expected value and peak value from the logarithmic normal distribution function as well as UIM, are sensitive to the changes in aggregate gradation types and NMAS of asphalt mixtures. Overall, this study provides a solid foundation for future research in exploring the internal structure of asphalt mastic within the mixtures.

Keywords:

• mastic distribution
• aggregate gradation
• image processing
• asphalt mixtures