Investigation on the fractal characteristic of asphalt pavement texture roughness incorporating 3D reconstruction technology

Han-Cheng Dan; Yongcheng Long; Hui Yao; Songlin Li; Yanhao Liu; Quanfeng Zhou; Han-Cheng Dan; Yongcheng Long; Hui Yao; Songlin Li; Yanhao Liu; Quanfeng Zhou

doi:10.3934/era.2023119

Electronic Research Archive

2023, Volume 31, Issue 4: 2337-2357. doi: 10.3934/era.2023119

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Investigation on the fractal characteristic of asphalt pavement texture roughness incorporating 3D reconstruction technology

1.
School of Civil Engineering, Central South University, Changsha, Hunan 410075, China
2.
Rail Data Research and Application Key Laboratory of Hunan Province, Central South University, Changsha, Hunan 410075, China
3.
Beijing Key Laboratory of Traffic Engineering, Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
4.
Zhonghai Construction Co., Ltd, Shenzhen, Guangdong 518057, China

Received: 29 November 2022 Revised: 16 February 2023 Accepted: 23 February 2023 Published: 27 February 2023

The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface.
- texture roughness,
- 3D reconstruction technology,
- fractal dimension,
- pavement texture,
- equivalent grayscale,
- mean texture depth
Citation: Han-Cheng Dan, Yongcheng Long, Hui Yao, Songlin Li, Yanhao Liu, Quanfeng Zhou. Investigation on the fractal characteristic of asphalt pavement texture roughness incorporating 3D reconstruction technology[J]. Electronic Research Archive, 2023, 31(4): 2337-2357. doi: 10.3934/era.2023119

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Abstract

The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface.

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