Computational method of 3D aggregate angularity based on CT images

CUI Zhe; ZHANG Sheng-rui

Volume 17 Issue 5

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2017 > 17(5): 39-49

Next Previous

CUI Zhe, ZHANG Sheng-rui. Computational method of 3D aggregate angularity based on CT images[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 39-49.

Citation:

CUI Zhe, ZHANG Sheng-rui. Computational method of 3D aggregate angularity based on CT images[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 39-49.

CUI Zhe, ZHANG Sheng-rui. Computational method of 3D aggregate angularity based on CT images[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 39-49.

Citation:

CUI Zhe, ZHANG Sheng-rui. Computational method of 3D aggregate angularity based on CT images[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 39-49.

PDF( 4137 KB)

Computational method of 3D aggregate angularity based on CT images

CUI Zhe^{1, 2
,},
ZHANG Sheng-rui^1
,

1.
School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Shaanxi Provincial Transport Planning Design and Research Institute, Xi'an 710065, Shaanxi, China

More Information

Author Bio:
CUI Zhe(1978-), male, senior engineer, doctoral student, 723648316@qq.com

ZHANG Sheng-rui(1961-), male, professor, PhD, 723648316@qq.com
Received Date: 2017-06-08
Publish Date: 2017-10-25

Abstract

Abstract

To improve the evaluation accuracy of aggregate angularity, a computational method of3 D aggregate angularity was put forward.Based on CT technology and 3 D reconstruction technology, image intensification and sharpen filter were used to clearly display the aggregates in aggregate CT images, and the gray thresholds of enhanced CT images were calculated and divided.MIMICS was used to reconstruct the 3 D models of aggregates in Marshal specimens.The roughness degree and sphericity degree of aggregate were put forward and taken as evaluation indexes to evaluate aggregate angularity based on the 3 D model.The influence factorsof 3 D model reconstruction were analyzed.Computational result shows the gray values of aggregate, asphalt and pore in AC-16 Marshall specimens are 101.32-170.14, 4.32-101.32, and0-4.32, respectively, so, image intensification and sharpen filter can clearly display aggregates in CT images and improve the 3 Dreconstruction accuracy of aggregate.The computational standard deviation of sphericity degrees is 0.000 7 when the CT images are dealt with by 2 pixels×2 pixels and 3 pixels×3 pixels sharpen filters, and the standard deviation of sphericity degrees is 0.042 3 when the CT images are dealt with by 5 pixels×5 pixels, 6 pixels×6 pixels and 7 pixels×7 pixels sharpen filters.Therefore, 2 pixels×2 pixels or 3 pixels×3 pixels sharpen filter should be adopted to dealt with the CT images in order to ensure the low fluctuation of computed sphericity degree.When sampling point densities are 50 and 70 per cubic millimeter, the standard deviation of roughness degrees is 0.001 6, but when sampling point densities are 5, 15 and 25 per cubic millimeter, the standard deviation of roughness degrees is 0.034 9, so, the density range of sampling points in CT images should be 50-70 per cubic millimeter to ensure that the 3 D models can reflect the real shapes of aggregates precisely.The standard deviations of 2 D sphericity degrees and roughness degrees based on 5 CT cross sections are 0.012 1-0.048 2, which reflects the larger variability and deviation.However, the roughness degrees of aggregates 1-5 calculated by the method based on the 3 D models are 0.991 2, 1.032 1, 0.974 2, 1.075 1, 1.043 2, respectively, and the corresponding average 2 Droughness degrees of aggregates 1-5 are 0.994 1, 1.023 9, 0.988 3, 1.097 5 and 1.060 8, respectively, which shows the 3 Dand 2 Droughness degrees are basically same.Obviously, the computational method based on the 3 D model used to calculate the roughness degree and sphericity degree fully considers the 3 D angularity of aggregate, the calculation result is not affected by the selected CT cross sections, and there are no calculation variability and deviation.
- pavement material,
- aggregate angularity,
- CT technology,
- 3Dreconstruction,
- sharpen filter,
- gray threshold

FullText(HTML)

References(26)

References

[1]	KUTAY M E, OZTURK H I, ABBAS A R, et al. Comparison of 2Dand 3Dimage-based aggregate morphological indices[J]. International Journal of Pavement Engineering, 2011, 12 (4): 421-431. doi: 10.1080/10298436.2011.575137
[2]	TUTUMLUER E, PAN Tong-yan. Aggregate morphology affecting strength and permanent deformation behavior of unbound aggregate materials[J]. Journal of Materials in Civil Engineering, 2008, 20 (9): 617-627. doi: 10.1061/(ASCE)0899-1561(2008)20:9(617)
[3]	HU Li-qun, YUN Di, LIU Zhuang-zhuang, et al. Effect of three-dimensional macrotexture characteristics on dynamic frictional coefficient of asphalt pavement surface[J]. Construction and Building Materials, 2016, 126: 720-729. doi: 10.1016/j.conbuildmat.2016.09.088
[4]	ZHU Feng, LIAO Jia-xin, XUE Sheng-guo, et al. Evaluation of aggregate microstructures following natural regeneration in bauxite residue as characterized by synchrotron-based X-ray micro-computed tomography[J]. Science of the Total Environment, 2016, 573: 155-163. doi: 10.1016/j.scitotenv.2016.08.108
[5]	MIN Yong-zhi, YIN Chao, DANG Jian-wu, et al. Fast recognition method of rail region based on hue value mutation feature of image[J]. Journal of Traffic and Transportation Engineering, 2016, 16 (1): 46-54. (in Chinese). doi: 10.3969/j.issn.1671-1637.2016.01.006
[6]	TONG Zheng, GAO J, ZHANG Hai-tao. Innovation for evaluating aggregate angularity based upon 3Dconvolutional neural network[J]. Construction and Building Materials, 2017, 155: 919-929. doi: 10.1016/j.conbuildmat.2017.08.129
[7]	RAJAN B, SINGH D. Understanding influence of crushers on shape characteristics of fine aggregates based on digital image and conventional techniques[J]. Construction and Building Materials, 2017, 150: 833-843. doi: 10.1016/j.conbuildmat.2017.06.058
[8]	RAVANSHAD A, ROQUE R, TEBALDI G, et al. Evaluation of two-dimensional gray-scale images for microtexture analysis of aggregate surface[J]. Journal of Materials in Civil Engineering, 2016, 28 (8): 1-9.
[9]	HAN Jian-den, LIU Wei-qing, WANG Shu-guang et al. Effects of crack and ITZ and aggregate on carbonation penetration based on 3D micro X-ray CT microstructure evolution[J]. Construction and Building Materials, 2016, 128: 256-271. doi: 10.1016/j.conbuildmat.2016.10.062
[10]	CHUNG Sang-yeop, HAN Tong-Seok, YUN Tae-sup, et al. Evaluation of the anisotropy of the void distribution and the stiffness of lightweight aggregates using CT imaging[J]. Construction and Building Materials, 2013, 48: 998-1008. doi: 10.1016/j.conbuildmat.2013.07.082
[11]	ZHAO Guo-tang, SHE Wei, YANG Guo-tao, et al. Mechanism of cement on the performance of cement stabilized aggregate for high speed railway roadbed[J]. Construction and Building Materials, 2017, 144, 347-356. doi: 10.1016/j.conbuildmat.2017.03.194
[12]	GARBOUT A, MUNKHOLM L J, HANSEN S B. Temporal dynamics for soil aggregates determined using X-ray CT scanning[J]. Geoderma, 2013, 204-205: 15-22. doi: 10.1016/j.geoderma.2013.04.004
[13]	CHEN Si-yu, YANG X, YOU Zhan-ping, et al. Innovation of aggregate angularity characterization using gradient approach based upon the traditional and modified Sobel operation[J]. Construction and Building Materials, 2016, 120: 442-449. doi: 10.1016/j.conbuildmat.2016.05.120
[14]	TAFESSE S, ROBISON FERNLUND J M, SUN Wen-juan, et al. Evaluation of image analysis methods used for quantification of particle angularity[J]. Sedimentology, 2013, 60 (4): 1100-1110. doi: 10.1111/j.1365-3091.2012.01367.x
[15]	WANG Hai-nian, BU Yin, ZHOU Yang, et al. Research on influencing factors and its optimization of scanning asphalt mixture based on X-ray CT technology[J]. Journal of Highway and Transportation Research and Development, 2014, 31 (11): 9-15. (in Chinese). doi: 10.3969/j.issn.1002-0268.2014.11.002
[16]	TANG Bo-ming, GUO Peng, XIAO Qiao-lin, et al. Analysis of angularity of asphalt concrete recycled aggregate[J]. China Journal of Highway and Transport, 2015, 28 (1): 24-29. (in Chinese). doi: 10.3969/j.issn.1001-7372.2015.01.004
[17]	MASAD E, OLCOTT D, WHITE T, et al. Correlation of fine aggregate imaging shape indices with asphalt mixture performance[J]. Transportation Research Record, 2001 (1757): 148-156.
[18]	JU Yang, WANG Li, XIE He-ping, et al. Visualization of the three-dimensional structure and stress field of aggregated concrete materials through 3D printing and frozen-stress techniques[J]. Construction and Building Materials, 2017, 143: 121-137. doi: 10.1016/j.conbuildmat.2017.03.102
[19]	GUO Nai-sheng, YOU Zhan-ping, TAN Yi-qiu, et al. Determination method of optimum asphalt content in asphalt mixture under considering homogeneity[J]. Journal of Traffic and Transportation Engineering, 2017, 17 (1): 1-10. (in Chinese). doi: 10.3969/j.issn.1671-1637.2017.01.001
[20]	MA Tian-shou, CHEN Ping. Study of meso-damage characteristics of shale hydration based on CT scanning technology[J]. Petroleum Exploration and Development, 2014, 41 (2): 227-233. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201402015.htm
[21]	JIANG Ji-wei, XIANG Wei, ZHANG Xue-yang. Research on mechanical parameters of intact sliding zone soils of Huangtupo landside based on CT scanning and simulation tests[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30 (5): 1025-1033. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201105021.htm
[22]	JIN Can, WANG Xin-lei, LIU Kai, et al. Mesoscopic modeling of asphalt mixture and numerical simulation research progress based on CT images[J]. Highway, 2016 (9): 232-239. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201609056.htm
[23]	FENG Jie, YU Ji-yu. Determination fractal dimension of soil macropore using computed tomography[J]. Journal of Irrigation and Drainage, 2005, 24 (4): 26-28, 40. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GGPS200504006.htm
[24]	GAO Jie, WANG Zhen-jun, ZHANG Ting, et al. Dispersion of carbon fibers in cement-based composites with different mixing methods[J]. Construction and Building Materials, 2017, 134: 220-227. doi: 10.1016/j.conbuildmat.2016.12.047
[25]	WANG Hai-nian, HAO Pei-wen, XIAO Qing-yi, et al. Digital image evaluation method for angularity of coarse aggregates[J]. Journal of Southeast University: Natural Science Edition, 2008, 38 (4): 637-641. (in Chinese). doi: 10.3321/j.issn:1001-0505.2008.04.018
[26]	LI Jia, LIN Hui. Quantification research of coarse aggregate angularity based on digital image processing[J]. Journal of Highway and Transportation Research and Development, 2008, 25 (7): 27-31. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200807005.htm