Image analysis of aggregate, mastic and air void phases for asphalt mixture

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Department of Applied Engineering and Technology, Morehead State University, Morehead 40351, Kentucky, USA
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Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an 710064, Shaanxi, China
3.
Department of Civil and Environmental Engineering, Michigan Technological University, Houghton 49931, Michigan, USA

Funds:

National Natural Sciencc Foundation of China 51250110077

More Information

Author Bio:
ADHIKARI S，Malc，Assistant Profcssor of Morchcad Statc University，s.adhikari@morcheadstatc.edu.

Article Text (Baidu Translation)

摘要

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Abstract: The shape characterization and spatial distribution of aggregate, mastic and air void phases for asphalt mixture were analyzed. Three air void percentage asphalt mixtures, 4%, 7% and 8%, respectively, were cut into cross sections and polished. X-ray scanning microscope was used to capture aggregate, mastic, air void phase by the image. The average of polygon diameter was chosen as a threshold to determine which aggregates would be retained on a given sieve. The aggregate morphological image from scanned image was utilized by digital image processing method to calculate the gradation of aggregate and simulate the real gradation. Analysis result shows that the air void of asphalt mixture has influence on the correlation between calculation gradation and actual gradation. When comparing 4.75 mm sieve size of 4%, 7% and 8% air void asphalt mixtures, 7% air void asphalt mixture has 55% higher than actual size gradation, 8% air void asphalt mixture has 8% higher than actual size gradation, and 4% air void asphalt mixture has 3.71% lower than actual size gradation. 4% air void asphalt mixture has the best correlation between calculation gradation and actual gradation comparing to other specimens. The air void percentage of asphalt mixture has no obvious influence on the air void orientation, and three asphalt mixtures show the similar air orientation along the same direction.
- road engineering /
- asphalt mixture /
- digital image processing /
- air void distribution /
- gradation

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Figure 1. Illustration of compacted cut sample

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Figure 2. 60 mm×60 mm classified images from images A, B, C

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Figure 3. Grey scale conversion from RGB color scale

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Figure 4. Number of pixel vs. density index

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Figure 5. Air void, aggregate and mastic domains of asphalt specimen using images A, B, C

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Figure 6. Aggregate structures of raw image and after image processing

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Figure 7. Air void orientations of different images

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Table 1. Gradation of aggregate

Sieve size/mm	Percentage passing
19	100.0
12.5	85.3
9.5	71.0
4.75	43.8
2.36	25.9
1.18	17.5
0.6	13.3
0.3	9.6
0.15	6.8
0.075	5.3
Pan	0.0

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Table 2. Specific gravity and air void level

Sample's ID	Targeted air void/%	Gmb	Gmm	Calculated air void/%
10-8	10	2.265	2.499	9.35
7-9	7	2.291	2.499	8.31
4-2	4	2.407	2.499	3.69

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Table 3. Calculated volume percentages of air void, aggregate and mastic domains

Image	Mastic/%	Aggregate/%	Air void/%
A	25.70	71.52	2.78
B	20.71	71.11	8.18
C	18.29	68.93	12.79

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Table 4. Comparison of actual gradation with image analysis

Sieve size	Actual retaining/%	Retaining on image A/%	Retaining on image B/%	Retaining on image C/%
19	0.0	0.00	0.00	0.00
12.5	14.7	11.05	8.84	19.01
9.5	14.3	22.32	12.00	20.75
4.75	27.2	26.19	42.17	29.36
2.36	17.9	13.25	13.98	11.19
1.18	8.4	7.65	6.35	4.50
0.6	4.2	2.58	2.65	1.78
0.3	3.7	1.02	0.90	0.78
0.15	2.8
0.075	1.5
Pan	5.3

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