Applicability of a new method for mesoscopic structure segmentation of asphalt mixture based on two-dimensional image

Abstract

The mechanical properties of the asphalt mixture largely depend on its internal structure. Studies in mixture mesostructure are of great significance for perfecting relevant theoretical systems and improving mixture design methods. However, efficient segmentation of mesostructure is an important precondition for mesoscale research about asphalt mixtures. Therefore, this article proposes a new method for mesostructure information acquisition that mainly improves the function of image preprocessing and separation of touching objects. A visual and quantitative comparison between the proposed and common methods is adopted. The result shows that the proposed method performs better in identifying small particles and retaining the fidelity of the boundary. Besides, the mesostructure of four types of asphalt mixtures are compared based on the proposed method. The result shows that the optimum sample size, which makes the measured 2D information efficiently close to the actual 3D information, is around 20–30. Compared to SUP25, the coordination number of AC20 is about 0.15–0.23 higher. Compared with the samples compacted by Marshall, the samples compacted by gyratory have a larger coordination number, about 0.12–0.21 higher, a larger fractal dimension, about 0.059–0.062 higher, a smaller uniformity of major axis orientation, about 1.53–1.28 lower, and a smaller uniformity of area distribution, about 0.063–0.12 lower.