Quantitative evaluation for shape characteristics of aggregate particles based on 3D point cloud data

Abstract

The aggregate with good shape characteristics are conducive to the formation of the internal structure of asphalt pavement and have an important impact on the pavement performance. In order to quantify the aggregate shape characteristics more comprehensively, the quantitative evaluation method for the shape of aggregate particles based on 3D point cloud data was proposed. Basalt, granite and limestone aggregates with size of 9.5 mm, 13.2 mm and 16 mm were obtained by screening. Each type of aggregates was divided into five different shapes of the tetrahedron, pentahedron, hexahedron, elongate and flake. The image acquisition system based on Gocator 3D intelligent sensor was used to collect 3D point cloud data of the aggregates. The noise in the 3D point cloud data collected was removed by the cascade filtering method and then the greedy triangulation algorithm was used to reconstruct the 3D aggregate digitized surface. Finally, the characteristic parameters such as length, width, height, volume and the maximum diameter of the 3D convex hull were extracted from the reconstructed aggregate particles, and the shape indexes of the aggregate, such as the isometric ratio, flakiness ratio, sphericity and roundness indexes, were calculated based on the extracted characteristics. The experimental results show that the 3D shape index of road aggregates proposed in this paper can be used for the comprehensive quantitative evaluation of the morphological characteristics of aggregates with different lithology, particle sizes and shapes. It is instructive to improve the performance of the asphalt mixture and study the aggregate gradation.