Influence of the Chemical Composition of Asphalt and the 3D Morphology of the Aggregate on Contact Surface Adhesion

The influence of the chemical composition of asphalt, the aggregate lithology, and the morphological characteristics of the aggregate on the level of adhesion between the asphalt and the aggregate is investigated. A contactless three-dimensional (3D) white-light scanning technique is used to obtain point cloud data of the aggregate particles. Six independent feature parameters are used as evaluation indices to quantitatively describe the multilevel features of the 3D morphology of road aggregates. Methods for analyzing the feature parameters based on the point cloud data of the aggregate are presented. Subsequently, the process and evaluation standard of the adhesion test are improved to quantify the spalling degree of the asphalt film on the aggregate surface under boiling conditions. The influences of the chemical composition of the asphalt and the aggregate morphology on the level of adhesion between the asphalt and aggregate are analyzed, and the compatibility between aggregates with different lithologies and the asphalt is assessed. The results show that the shape factor (SF) can be used to characterize the needle-flake shape of the particles, the ellipsoid index (E) is suitable to determine the angularity of the aggregate particles, and the 3D joint roughness coefficient (JRC3D) describes the roughness of the particle surface. The type of adhesion between the aggregate and the asphalt includes chemical and physical adsorption; chemical bonding is relatively strong, and the physical orientation force and mechanical interlocking force are relatively weak. Alkaline limestone aggregates should be used with asphalt with a high aggregate content of colloid and asphaltenes. Basalt aggregate with weak alkalinity should be used with asphalt with a high colloid content, and the use of angular aggregates should be avoided. Acidic aggregates with compact shapes, few edges and corners, and rough surfaces should be used prudently.

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

Construction and Building Materials ◽

10.1016/j.conbuildmat.2020.120156 ◽

2020 ◽

Vol 263 ◽

pp. 120156

Author(s):

Zhaoyun Sun ◽

Chaofan Wang ◽

Xueli Hao ◽

Wei Li ◽

Xin Zhang

Keyword(s):

Quantitative Evaluation ◽

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Shape Characteristics ◽

Aggregate Particles

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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