Abstract:

The angularity of the aggregate particles is an important factor determining the performance of asphalt mixes for road use． In this paper，firstly，a 3D image acquisition system based on Gocator 3D intelligent sensor was built to obtain 3D point cloud data of 3 aggregate samples of basalt，granite and limestone with particle sizes of 9. 5 mm，13. 2 mm and 16. 0 mm． Then，the Sobel-Feldman convolution method and the aggregate surface normal method was used to evaluate the surface angularity of aggregate particles，and the two methods were compared with the existing AIMS gradient angularity evaluation method． The results show that the quantitative method of aggregate particle surface angularity based on Sobel-Feldman convolution is more accurate． In addition，the sharper the aggregate is，the larger the laggregate angularity index and the number of normal clusters are; the more round the aggregate is，the smaller the aggregate angularity index and the number of normal clusters are．

Key words: aggregate particle, 3D point cloud model, angularity quantification, Sobel-Feldman convolution, surface normal