journal article·2024-08-02

Reconstruction and evolution of 3D model on asphalt pavement surface texture using digital image processing technology and accelerated pavement testing

XiaoPing Ji, Shiyu Zhu, Yunlong Sun, Hangle Li, Ye Chen, Yun Chen

Publisher page

Abstract

Asphalt pavement surface texture is the main factor affecting pavement function. Reconstruction of the asphalt pavement surface texture is needed to accurately reveal its evolutionary characteristics for pavement performance and quality evaluation. To reconstruct an optimised 3D model of the asphalt pavement surface texture and to study its evolutionary properties, digital image processing technique and accelerated pavement testing system were used. First, the asphalt pavement surface texture 3D model optimised by three camera parameters and their thresholds. Next, the accelerated pavement testing system simulated traffic loadings on dense-gradation asphalt mixtures to investigate the pavement surface texture evolution properties. Finally, predictive models are developed for the asphalt pavement surface texture evolution. Results show that the optimised pavement texture 3D model resembles actual pavement structure. The surface texture evolutionary characteristics of asphalt pavement can be divided into three periods and six stages. The evolution model can accurately characterise the evolution of the surface texture of asphalt pavement.Abbreviations: MLS11: Accelerated pavement testing system; HP: Mean pixel difference; Df: Fractal Dimension; MTD: Mean Texture Depth; BPN: British Pendulum Number
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Infrastructure Maintenance and Monitoring · Advanced Numerical Analysis Techniques · Asphalt Pavement Performance Evaluation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang’an University, Xi’an, Shaanxi Province, People’s Republic of China

Xinjiang Transportation Planning Surveying and Design Institute, Urumqi, Xinjiang, People’s Republic of China

Tian Jin Municipal Engineering Design & Research Institute Co, Ltd., Shenzhen, Guangdong, People’s Republic of China

Funding

Fundamental Research Funds for the Central Universities · Innovation Capability Support Program of Shaanxi · National Natural Science Foundation of China

References (38)

Show source-provided references
1. 10.1016/j.wear.2006.08.04510.1016/j.wear.2006.08.045 ↗
2. 10.3390/infrastructures603004110.3390/infrastructures6030041 ↗
3. 10.1016/j.con-buildmat.2019.07.32110.1016/j.con-buildmat.2019.07.321 ↗
4. 10.1016/j.sbspro.2012.09.95310.1016/j.sbspro.2012.09.953 ↗
5. 10.1016/j.conbuildmat.2019.11669310.1016/j.conbuildmat.2019.116693 ↗
6. 10.1080/14680629.2020.182635010.1080/14680629.2020.1826350 ↗
7. 10.1080/10298436.2020.183782610.1080/10298436.2020.1837826 ↗
8. 10.3846/20296991.2013.78688110.3846/20296991.2013.786881 ↗
9. 10.1016/j.wear.2020.20333910.1016/j.wear.2020.203339 ↗
10. 10.1080/14680629.2012.74980410.1080/14680629.2012.749804 ↗
11. 10.1016/j.conbuildmat.2016.04.00210.1016/j.conbuildmat.2016.04.002 ↗
12. 10.1016/j.conbuildmat.2018.01.17110.1016/j.conbuildmat.2018.01.171 ↗
13. Mcgowan R. Feighan K. Mulry B. Casey T. Millar P. & Woodward D. (2018). Use of Close-Range Photogrammetry to Characterize Texture in a Pavement Surfacing Material.
14. 10.1016/j.compeleceng.2009.10.00210.1016/j.compeleceng.2009.10.002 ↗
15. Neaylon K. L. (1996). The measurement of aggregate reorientation in sprayed seals using stereoscopic micro photogrammetry [A]. Combined 18th ARRB Transport Research Conference and Transit New Zealand Land Transport Symposium [C] Christchurch NZ ARRB Transport Research Ltd.
16. Partl, M. N., Raab, C., & Arraigada, M. (2015). Innovative asphalt research using accelerated pavement testing. Journal of Marine Science & Technology, 23, 269–280.
17. 10.1016/j.wear.2021.20375710.1016/j.wear.2021.203757 ↗
18. 10.1016/j.conbuildmat.2017.05.05010.1016/j.conbuildmat.2017.05.050 ↗
19. 10.1109/MSP.2008.92309310.1109/MSP.2008.923093 ↗
20. 10.1080/10298436.2011.64379310.1080/10298436.2011.643793 ↗
21. 10.1080/10298436.2022.209985410.1080/10298436.2022.2099854 ↗
22. Shalaby, A., & Gendy, A. E. (2008). Three-dimensional pavement surface macrotexture measurements using the photometric steroe technique and applications. Symposium on Pavement Surface Characteristics.
23. Shen, A., Liu, B., Guo, Y., Guo, Y., Peng, Y. U., Muyang, Y. U., & Highway, S. O. (2019). Skid resistance attenuation of steel slag asphalt mixtures on tunnel pavement. Journal of Building Materials, 22, 8.
24. 10.1061/(ASCE)MT.1943-5533.000323410.1061/(ASCE)MT.1943-5533.0003234 ↗
25. 10.1016/j.wear.2006.08.04510.1016/j.wear.2006.08.045 ↗
26. Specht, L. P., Khatchatourian, O., & Santos, R. (2013). Measurement of pavement macrotexture through digital image processing. Universidade Estadual de Maringá, 35, 31–38.
27. 10.3141/2575-1310.3141/2575-13 ↗
28. 10.1016/j.jtte.2015.01.00110.1016/j.jtte.2015.01.001 ↗
29. 10.1080/14680629.2015.109090910.1080/14680629.2015.1090909 ↗
30. 10.1016/j.wear.2014.06.00510.1016/j.wear.2014.06.005 ↗
31. 10.1016/j.wear.2013.09.01310.1016/j.wear.2013.09.013 ↗
32. Wang, D., Li, W., & Zhang, X. (2004). Evaluation and measurement of asphalt pavement surface texture depth with digital image technique. Journal of South China University of Technology, 32, 42–45.
33. Wang, D., & Qian, C. (2016). Research of the surface texture properties of asphalt pavement based on the industrial CT technology. Science Technology and Engineering, 16, 270–274.
34. 10.1016/j.trc.2010.12.00110.1016/j.trc.2010.12.001 ↗
35. 10.1016/j.jclepro.2023.13651510.1016/j.jclepro.2023.136515 ↗
36. 10.1016/j.conbuildmat.2014.07.01510.1016/j.conbuildmat.2014.07.015 ↗
37. 10.1520/JTE2021064910.1520/JTE20210649 ↗
38. Zhou, X., Xiao, S., Liu, W., Huang, X., & Xiao, W. (2018). Multifractal characteristics and polishing behaviors of surface texture on asphalt pavement. Journal of Southeast University (Natural Science Edition), 48, 6.