Evaluating Angularity of Coarse Aggregates Using the Virtual Cutting Method Based on 3D Point Cloud Images

Hanye Liu,Ju Huyan,Wei Li,Xueli Hao,Zhaoyun Sun,Meng Guo

doi:10.1109/access.2020.3013901

Abstract

In this paper, a new method called the Virtual Cutting Method is proposed to evaluate the angularity index (AI) values of 3D point cloud coarse aggregate images with the aim of characterizing the angularity of aggregates on conveyor belts. The 3D point cloud images of coarse aggregates were first captured, preprocessed, and segmented into single 3D aggregate objects. Based on the processed 3D aggregate images, intersection contours were extracted using a series of intersection planes with an equivalent angle between two adjacent planes. The AI was evaluated by averaging the angularity of the contours using the gradient method, which was used in the AIMS2 system. Statistical analysis was then performed to select the optimum angle between two adjacent planes. It was found that an angle of five degrees was the ideal angle, as it can balance the execution time and effectiveness of the method. Finally, the AI results of the Virtual Cutting Method were compared with those of 2D and 3D Projection Methods. It was found that the AI rankings of the three methods for different aggregate textures are generally consistent. The findings of this study conclude that the Virtual Cutting Method can be employed to quantify the angularity of a single aggregate or aggregates in piles on conveyor belts based on 3D point cloud images.

Highlights

As one of the most important ingredients of construction materials in both asphalt concrete and hydraulic cement concrete, the morphological characteristics of aggregates, including the shape, angularity and texture (Figure 1), significantly affect the performance of pavement systems [1]–[4]
PERFORMANCE OF THE VIRTUAL CUTTING METHOD As previously discussed, the intersection contour was extracted based on a series of adjacent cutting planes, and the angle degrees between the adjacent cutting planes were set to a fixed value
The aim of this paper was to evaluate the angularity index (AI) of aggregates on conveyor belts based on 3D upper-side point cloud images

Summary

Introduction

As one of the most important ingredients of construction materials in both asphalt concrete and hydraulic cement concrete, the morphological characteristics of aggregates, including the shape, angularity and texture (Figure 1), significantly affect the performance of pavement systems [1]–[4]. Coarse aggregate angularity is defined as the sharpness of corners/nicks and can improve the shear strength properties of hot-mix asphalt (HMA) and unbound aggregate base layers [5]–[7]. Pavement performance indicators affected by aggregate angularity include the dosage of binder, dynamic modulus, high-temperature stability, and. To guarantee the critical performance of the pavement, the particle angularity should be well assessed. In a pavement management system, the measurement of aggregate angularity is mainly based on human operation and the operators’ experience, which is criticized as tedious, time-consuming, and subjective [8], [9].

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Conclusion