Volume 23 Issue 2
Apr.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(2): 92-102
WANG Hai-nian, WAN Tong-tong, LIU Yuan-yuan, ZHENG Wen-hua, GAO Jun-feng. Optimization on evaluation indicators of asphalt pavement surface segregation based on smartphone image acquisition method[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 92-102. doi: 10.19818/j.cnki.1671-1637.2023.02.006
Citation: WANG Hai-nian, WAN Tong-tong, LIU Yuan-yuan, ZHENG Wen-hua, GAO Jun-feng. Optimization on evaluation indicators of asphalt pavement surface segregation based on smartphone image acquisition method[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 92-102. doi: 10.19818/j.cnki.1671-1637.2023.02.006
shu

Optimization on evaluation indicators of asphalt pavement surface segregation based on smartphone image acquisition method

doi: 10.19818/j.cnki.1671-1637.2023.02.006
  • 1.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Civil Engineering and Architecture, Nanchang Hangkong University, Nanchang 330063, Jiangxi, China

  • 3.

    School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Key Research and Development Program of China 2021YFB2601000

National Natural Science Foundation of China 52078048

National Natural Science Foundation of China 51878063

More Information
  • Author Bio:

    WANG Hai-nian(1977-), male, professor, PhD, wanghn@chd.edu.cn

  • Received Date: 2022-11-03
    Available Online: 2023-05-09
  • Publish Date: 2023-04-25
    Abstract
  • In order to optimize the evaluation indicators of asphalt pavement surface segregation, three different gradation types of asphalt mixture slab specimens were prepared in laboratory. An image acquisition method was proposed to obtain the surface images of asphalt mixture by using smartphone. The box-counting method and multifractal spectrum algorithm were used to calculate the fractal dimension and multifractal spectrum indexes of the binary image. Image-Pro Plus software was adopted to extract the concave distribution percentage and macro-structure width. The surface texture depth was tested by using the indoor sand patching method, and the mean texture depth was calculated to evaluate asphalt mixture surface segregation. The concave distribution characteristics of asphalt mixture surfaces with different gradations were analyzed. The grey relational entropy of the evaluation indicators of asphalt mixture surface segregation was studied. Research results show that the error rate of reliability analysis for obtaining binary images based on smartphones is less than 3%, indicating that the obtaining method has high repeatability. The concave asphalt mixed surface has considerable fractal characteristic, the larger the aggregate particle size, the more complex surface structure analysis is. There is a linear correlation between the evaluation indicators of asphalt pavement surface segregation based on image processing method and the mean texture depth, but the correlation varies. The surface fractal characteristic indicator of the identical asphalt mixture has wide error range, however, the concave distribution percentage index has a minimum error range, changing from -1.89% to 1.89%. The entropy correlation degree between concave distribution percentage and mean texture depth is highest, which is 0.996 2, and followed by the indexes of multifractal spectrum, fractal dimension and macro-structure width. Thus, the concave distribution percentage is recommended as a reliable indicator for evaluating asphalt mixture surface segregate.

     

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