Volume 23 Issue 2
Apr.  2023
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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

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
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
  • 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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  • [1]
    CHI Feng-xia, ZHANG Xiao-ning, XUE Zhong-jun, et al. Evaluation method of surface segregation of asphalt pavement based on laser texture measurer[J]. China Journal of Highway and Transport, 2008, 21(5): 1-5. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200805002.htm
    [2]
    YU Miao, YOU Zhan-ping, WU Guo-xiong, et al. Measurement and modeling of skid resistance of asphalt pavement: a review[J]. Construction and Building Materials, 2020, 260: 119878. doi: 10.1016/j.conbuildmat.2020.119878
    [3]
    CHRTSTOPHER WILLIAMS R, DUNCAN G, WHITE T. Sources, measurements, and effects of segregated hot mix asphalt pavement[R]. West Lafayette: Purdue University, 1996.
    [4]
    BROWN E R, COLLINS R, BROWNFIELD J R. Investigation of segregation of asphalt mixtures in the state of Georgia[J]. Transportation Research Record, 1989, 1217: 1-8.
    [5]
    STROUP-GARDINER M, BROWN E R. Segregation in Hot-Mix Asphalt Pavements[M]. Washington DC: Transportation Research Board, 2000.
    [6]
    HUANG Qi, ZHAO Xin, LEI Yu, et al. Relationship between gradation type and segregation degree for asphalt mixture[J]. Journal of Traffic and Transportation Engineering, 2009, 9(2): 1-6. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2009.02.001
    [7]
    HAO Xue-li, SHA Ai-min, SUN Zhao-yun, et al. Evaluation and comparison of real-time laser and electric sand-patch pavement texture-depth measurement methods[J]. Journal of Transportation Engineering, 2016, 142(7): 04016022. doi: 10.1061/(ASCE)TE.1943-5436.0000842
    [8]
    WANG Duan-yi, LI Wei-jie, ZHANG Xiao-ning. Evaluation of surface segregation of asphalt pavement by using digital image technique[J]. Journal of South China University of Technology (Natural Science Edition), 2005, 33(1): 16-20, 26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG200501003.htm
    [9]
    KHASAWNEH M A. Estimation of asphalt pavement surfaces using image analysis technique[J]. International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 2014, 8(6): 582-587.
    [10]
    WANG Hai-nian, BU Yin, WANG Yan-zhe, et al. The effect of morphological characteristic of coarse aggregates measured with fractal dimension on asphalt mixture's high-temperature performance[J]. Advances in Materials Science and Engineering, 2016, 2016: 1-9.
    [11]
    MIAO Ying-hao, WU Jia-qi, HOU Yue, et al. Study on asphalt pavement surface texture degradation using 3-D image processing techniques and entropy theory[J]. Entropy (Basel), 2019, 21(2): 208. doi: 10.3390/e21020208
    [12]
    RAN Mao-ping, XIAO Shen-qing, ZHOU Xing-lin, et al. Evaluation of segregation in asphalt pavement surface using concave multifractal distribution[J]. Journal of Testing and Evaluation, 2018, 46(5): 20160616. doi: 10.1520/JTE20160616
    [13]
    ZHANG Ke, ZHANG Zheng-qi, LUO Yao-fei, et al. Accurate detection and evaluation method for aggregate distribution uniformity of asphalt pavement[J]. Construction and Building Materials, 2017, 152: 715-730. doi: 10.1016/j.conbuildmat.2017.07.058
    [14]
    CHEN De, ROOHI SEFIDMAZGI N, BAHIA H. Exploring the feasibility of evaluating asphalt pavement surface macro-texture using image-based texture analysis method[J]. Road Materials and Pavement Design, 2015, 16(2): 405-420. doi: 10.1080/14680629.2015.1016547
    [15]
    CONG Lin, SHI Jia-chen, WANG Tong-jing, et al. A method to evaluate the segregation of compacted asphalt pavement by processing the images of paved asphalt mixture[J]. Construction and Building Materials, 2019, 224: 622-629.
    [16]
    LIU Tao, ZHANG Xiao-ning, LI Zhi, et al. Research on the homogeneity of asphalt pavement quality using X-ray computed tomography (CT) and fractal theory[J]. Construction and Building Materials, 2014, 68: 587-598.
    [17]
    WANG Chang-heng, ZHOU Wu-jun. Evaluation of the homogeneity of asphalt pavement based on fractal method[J]. Highway Engineering, 2010, 35(1): 117-120. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGL201001026.htm
    [18]
    SONG Yong-chao, YAN Gong-xi, SUI Yong-qin, et al. Texture structure distribution of asphalt pavement surface based on digital image processing technology[J]. Journal of Central South University (Science and Technology), 2014, 45(11): 4075-4080. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201411049.htm
    [19]
    MIAO Ying-hao, SONG Ping-ping, GONG Xiu-qing. Fractal and multifractal characteristics of 3D asphalt pavement macrotexture[J]. Journal of Materials in Civil Engineering, 2014, 26(8): 04014033.
    [20]
    WANG Wei-feng, YAN Xin-ping, XIAO Wang-xin, et al. Approach of multifractal feature description and recognition for pavement texture[J]. Journal of Traffic and Transportation Engineering, 2013, 13(3): 15-21. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2013.03.003
    [21]
    ZHANG Zheng-qi, HUANG Shuo-lei, ZHANG Ke. Accurate detection method for compaction uniformity of asphalt pavement[J]. Construction and Building Materials, 2017, 145: 88-97.
    [22]
    CHU L, GUO Wei-wei, FWA T F. Theoretical and practical engineering significance of British pendulum test[J]. International Journal of Pavement Engineering, 2022, 23(1): 1-8.
    [23]
    SUN Zhao-yun, SHA Ai-min, YAO Qiu-ling, et al. Realization of threshold segmentation algorithm in asphalt mixture[J]. Journal of Chang'an University (Natural Science Edition), 2005, 25(6): 34-38. (in Chinese)
    [24]
    LOPES R, BETROUNI N. Fractal and multifractal analysis: a review[J]. Medical Image Analysis, 2009, 13(4): 634-649.
    [25]
    GIMENEZ D, POSADAS A, COOPER M. Multifractal characterization of soil pore shapes[J]. European Geosciences Union General Assembly, 2010, 12: 10649.
    [26]
    IMANI F, YAO Bing, CHEN Rui-min, et al. Joint multifractal and lacunarity analysis of image profiles for manufacturing quality control[J]. Journal of Manufacturing Science and Engineering, 2019, 141(4): 044501.
    [27]
    ZHANG Xiang, WANG Hai-nian, MOHD HASAN M R, et al. Traffic open time prediction of fog seal with sand using image processing technology[J]. Construction and Building Materials, 2019, 209(2): 9-19.
    [28]
    WAN Tong-tong, WANG Hai-nian, FENG Po-nan, et al. Concave distribution characterization of asphalt pavement surface segregation using smartphone and image processing based techniques[J]. Construction and Building Materials, 2021, 301: 124111.
    [29]
    GAO Jun-feng, WANG Hai-nian, YOU Zhan-ping, et al. Gray relational entropy analysis of high temperature performance of bio-asphalt binder and its mixture[J]. International Journal of Pavement Research and Technology, 2018, 11: 698-708.
    [30]
    ZHANG Qi-shan, GUO Xi-jiang, DENG Ju-long. Grey relation entropy analysis method[J]. Systems Engineering-Theory and Practice, 1996, 16(8): 7-11. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL608.001.htm

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