A preliminary study on the identification of microcracks on the aggregate surface of asphalt pavements under cumulative tire wear

This paper presents a microcrack segmentation method and two-dimensional planar parameters to quantitatively characterize microcracks in aggregates under tire loading. First, an aggregate sample with extensive repeated tire wear was collected from the pavement surface for rock CT scanning. The acquired grayscale images were pre-processed by image filtering and appropriate image enhancement. Then, a microcrack segmentation method for aggregate damage analysis was developed based on the edge region growth algorithm and compared with the traditional threshold segmentation method. Finally, based on the microcrack segmentation results, the distribution pattern of microscopic characteristic parameters of aggregate surface microcracks was revealed. The results show that the best image pre-processing scheme is to first reduce the noise in the gray-scale images by using the Gaussian and median filters together, and then enhance their local details by gamma transform. Compared with the traditional threshold segmentation method, the proposed method effectively improves the microcrack segmentation accuracy by setting a large number of seed positions and adjusting their growth patterns. Moreover, the microscopic characteristic parameter distribution pattern of aggregate microcracks in each layer suggests that the crack extension degree on the pavement aggregate surface directly subjected to tire loads is generally larger than that on other parts. In addition, the microcracks are not only densely distributed within the aggregate surface layer, but also tend to extend deeper, indicating that the effect of cumulative tire loads on frictional damage to the pavement surface needs to be investigated in the field of pavement skid resistance research.