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Internal structure characterizationof asphalt mixtures for ruttingperformance using imaging analysisNima Roohi Sefidmazgi a , Laith Tashman a & Hussain Bahia aa Department of Civil and Environmental Engineering, University ofWisconsin-Madison, WI, USAVersion of record first published: 24 Apr 2012.

To cite this article: Nima Roohi Sefidmazgi , Laith Tashman & Hussain Bahia (2012): Internalstructure characterization of asphalt mixtures for rutting performance using imaging analysis, RoadMaterials and Pavement Design, 13:sup1, 21-37

To link to this article: http://dx.doi.org/10.1080/14680629.2012.657045

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Road Materials and Pavement DesignVol. 13, No. S1, June 2012, 21–37

Internal structure characterization of asphalt mixtures for ruttingperformance using imaging analysis

Nima Roohi Sefidmazgi*, Laith Tashman and Hussain Bahia

Department of Civil and Environmental Engineering, University of Wisconsin-Madison, WI, USA

Characterization of the asphalt concrete microstructure using two-dimensional (2-D) imagingtechniques is an economically efficient approach. However, the features that have been capturedand quantified using 2-D imaging in most published research have been limited to simplisticanalyses of aggregate structure. The present research focused on introducing a more elaboratemethod of characterization of internal structure, and proposing new indices to relate to andexplain rutting resistance performance of asphalt mixtures. The aggregate internal structureprovides the skeleton of the asphalt concrete, which plays an important role in rutting resistance.It is shown that this structure can be captured using a combination of image analysis indicesdeveloped in this research, namely: number of aggregate-on-aggregate contact points, contactlength/area, and contact plane orientation. These parameters are defined for both the totalaggregates and for the effective load bearing aggregate structure, referred to as the ‘skeleton’in this study. Software developed in a previous study and significantly modified for this paper,is used to process digital images of a set of asphalt mixtures with different gradations, bindercontents, types of modification, and compaction efforts. The results demonstrate a correlationbetween the internal structure indices and the mixture rutting performance. Additionally, theindices were successfully used to capture the effect of compaction effort, gradation quality, andbinder modification on the mixture internal structure.

Keywords: flow number; rutting; aggregate skeleton; aggregate contact; image analysis;internal structure; HMA

1. IntroductionAsphalt concrete is a heterogeneous multiphase material that consists of aggregates, asphalt binder,and air voids. These components constitute a complex microstructure. Based on the contactmechanism analysis, Zhu and Nodes (2000) demonstrated that the transmission of load in theasphalt mixture is mainly determined by the interaction of aggregates and binder at the contactsof adjacent aggregates. According to Zhu and Nodes, changes in mechanical and geometricalparameters in aggregate and binder will affect the overall stress–strain distribution in an asphaltmixture. The contact-based stress–strain equations and models for asphalt mixtures show that thegeometrical properties of the contact such as contact area, number of contact zones and contactorientation affect the stress distribution in the mixture as a whole (Zhu, 1998; Zhu & Dass, 1996).It has been demonstrated that the directional distribution of the asphalt mixture micromechanicalproperties affects its response to loading (Tashman, Masad, Little, & Zabib, 2004; Wang et al.,2004). Additionally, it has been shown that the aggregate gradation affects the internal structureand the stress distribution in asphalt mixtures (Masad, Muhunthan, Shashidhar, & Harman, 1999).

*Corresponding author. Email: Roohisefidma@wisc.edu

ISSN 1468-0629 print/ISSN 2164-7402 online© 2012 Taylor & Francishttp://dx.doi.org/10.1080/14680629.2012.657045http://www.tandfonline.com

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Previous research has demonstrated that asphalt mixture microstructure can be character-ized using imaging techniques (Masad, Muhunthan, Shashidhar, & Harman, 1998; Masad,Somadevan, Bahia, & Kose, 2000; Shashidhar, Zhong, Shenoy, & Bastian, 2000; Tashman,Wang, & Thyagarajan, 2007; Wang et al., 2004; Yue, Bekking, & Morin, 1995). Despite exten-sive research and advancements in technology for obtaining digital images of asphalt mixture,many challenges remain regarding the algorithms and methodologies used in the image analysis.Characterization of asphalt concrete using two-dimensional (2-D) imaging is an economicallyefficient approach compared with three-dimensional (3-D) analyses. The features that have beencaptured and quantified using 2-D images were limited to simplistic indices including the numberof contacts and aggregate orientation. A recent study by Coenen, Kutay, and Bahia (2011) hasintroduced advanced software for accurate image analysis of 2-D representation of aggregatesand asphalt in a mixture. However, this software, although significantly useful, is limited in theinternal structure analysis capabilities. The internal structure of asphalt mixtures in this softwareas well as others published fall short of fully representing aggregate interactions. There is there-fore a need for a more comprehensive set of indices that can capture and characterize the numberof contact zones combined with their lengths and orientations.

2. ObjectivesThe main objectives of this study are to develop and implement a set of internal aggregate structureanalysis features for asphalt mixtures based on 2-D image analysis that capture important featuresof aggregate interaction in asphalt mixtures, to define representative indices, and to determine therelationship between these image-based indices and rutting performance.

3. Internal structure parametersAggregates provide the skeleton of the asphalt concrete and carry most of the load. This structureis called the aggregate internal structure and can be represented using a combination of severalindices based on image analysis. The following list represents the internal structure indices thatare believed to dictate the rutting resistance of the mixture:

• number of aggregate contacts,• contact length/area,• normal to contact plane orientation,• stress paths within the skeleton (i.e., branches of connected aggregates in the axial direction,

which are anticipated to contribute in carrying most of the load).

The number of aggregate contacts in the asphalt mixture represents the connectivity of theinternal aggregate structure. Increasing the number of contact points leads to a better stress distri-bution with less stress concentrations. The effectiveness of contact between adjacent aggregatesis dependent on the contact area (i.e., contact length in 2-D images) and is normal to contact planeorientation (i.e., measured from horizontal axis). The increase in contact length increases thefriction and interlocking between aggregates. Additionally, the closer the normal to contact planeorientation to the loading direction, the more effective it is in resisting deformation under loading.

Based on stress transmission concepts, the aggregates that are connected (i.e., contacted) toeach other in the loading direction transfer a higher portion of the load to the underlying layersand the stress paths would mainly go through them. However, there are many single contactedaggregates (i.e. aggregates that have one contact with other aggregates) that are not connected

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Road Materials and Pavement Design 23

to the top or bottom of the sample and aggregates. These single contacted aggregates are notexpected to carry a significant amount of the loads. Therefore, the effective part of the aggregatesand the aggregate internal structure indices are those that are in the aggregate ‘skeleton’, whichis composed of connected aggregates in the loading direction.

Conceptually, when there are two mixes with different internal aggregate structure, the internalstructure’s indices are expected to represent the difference in performance, given that all otherparameters are held constant (i.e., density, aggregate type, binder type, VFA, VMA, etc). Often,this selection is done based on density measurements. However, research has also shown thatasphalt mixtures with the same density can have different performance (Coenen et al., 2011).Therefore, the internal structure can be a better discriminatory parameter in comparison to densityfor selection of mixtures that are more resistant to rutting.

3.1. Software developmentImage processing and quantification of the internal structure features were conducted using a2-D image processing software named ‘iPas’. The details of the image processing procedure areexplained in a previous publication by Coenen et al. (2011). The analysis in iPas is accomplishedusing watershed filtering, thresholding and hybrid max filtering (to remove Gaussian noises). Thevolumetric properties and gradation of the mixture were entered as an input to the software tocalculate the volume fraction of aggregates in the mix as an accuracy control of area fraction ofaggregates captured in the image. Based on a processed image, the software performs a virtualsieve analysis. Users can control the quality of aggregate structure captured based on comparisonsof the real and virtual gradation of the mixture and the volume (real) and area (virtual) fractionof aggregates in the mixture.

The last version of iPas software mostly focused on the aggregate orientation and numberof contact surfaces (zones) for microstructure characterization (Coenen et al., 2011). In orderto characterize the asphalt mixture’s internal structure more accurately, the software has beenimproved to quantify the newly proposed features (i.e., contact surfaces orientation, contact linelength (2-D), and aggregate skeleton).

The new indices defined in this paper were measured according to the following procedures.In the software, contact is defined when two aggregates’ perimeter pixels are within a distance

specified by the user and all the pixels of the two aggregates’ perimeter within this distance arecaptured. These pixels form the contact lines. For each pixel of aggregate number one there isone and only one pixel on the perimeter of aggregate number two with a distance less than thepredefined value (if there are several, the closest pixel). This procedure is depicted in Figure 1.The midpoint pixel of each pair of pixels from the two aggregates is considered as contact pixelsforming the contact line in 2-D images. It is known that the contact line in many aggregates willnot be continuous because of the rough nature of aggregate’s surface.

The results for contact length were verified for selected coarse aggregates using a caliper.Figure 1 shows an example of contact line for two aggregates.

Thereafter, the calculation of the normal to contact orientation (angle from horizontal axis) canbe performed. The implemented procedure connects the contact pixels using straight lines andcalculates the slope perpendicular to these straight lines. Thereafter, the vector that representsthese directions is determined, which defines the normal to contact orientation. Figure 2 showsthe procedure schematically.

Contact length and orientation are both important parameters to characterize the internal struc-ture of mixture. Differences in contact areas (length) produce different stress intensities andaggregate interlocking, consequently affecting performance. In addition, the contact orientationsdefine the effectiveness of contacts in carrying the load. The closer the direction of the normal to

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Agg1

Agg2

Agg1

Agg2

Contact Line

Predefined distance

Figure 1. Contact line.

Figure 2. Schematic of contact orientation calculation.

Figure 3. Contact lines and orientation in real mixtures. (Normal to contacts lines are shown with arrows).

contact orientation to that of axial loading (i.e., 90◦ in this case), the more effective the contactis in resisting the axial load. Figure 3 shows contact lines and orientation for a mix under axialloading.

As previously discussed, the aggregate skeleton is the structure of aggregates that are connectedin the loading direction (from top to bottom of the sample in this case). In order to measure theinternal structure indices of the skeleton, aggregates that are not in the skeleton were neglected

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Road Materials and Pavement Design 25

Figure 4. Aggregate skeleton- connectivity (lines represent stress paths).

Figure 5. Bailey’s method.

(i.e. single or single contacted aggregates, set of aggregates that are not connected to the aggregatesin the top and bottom of the mixture). Figure 4 shows a black and white image of a mix and theskeleton of aggregates represented in a contour image and lines that link the contact zones ofaggregates.

Validation of the software is needed to assess the newly developed indices. In order to verify theoutputs, first the software was used to characterize ideal images of microstructures. The analysisof the ideal images proved that the software algorithms of the indices work properly.

Following the initial validation, coarse graded mixtures with a few aggregate sizes were eval-uated using the developed software. The verification mixtures were designed based on Bailey’smethod (Olard & Perraton, 2010). According to this method, the voids produced by aggregatessize (i.e., diameter) of D, can be filled with aggregate size no greater than 0.22D (i.e., the averageof round-faced and flat-faced aggregates) for best aggregate packing in the mixture (Figure 5).

Two mixtures were designed, one based on Bailey’s method using aggregate sizes 12.5 mm,2.36 mm and passing #200 (i.e., referred to as dense packing mix) and the other using aggregatesizes 12.5 mm, 4.75 mm and passing #200 (i.e., referred to as loose packing mix, R2 > 0.22R1)(Gradations of both mixes are shown in Table 1 and Figure 6). Also, to verify the effect ofcompaction effort, samples of the loose mix were compacted at 50 and 100 gyrations of SuperpaveGyratory Compactor (SGC).

The samples were cut in three sections leading to attainment of six 2-D images, with onecutting section at the middle of the sample and two in the one inch distance from the middlesection (producing four slices of equal volumes) (Figure 7). Images of the three mixes are shownin Figure 8.

The nine images of three mixes (i.e., three images per mix – one image of each cutting faces)were scanned and analyzed using the newly developed software. All the images were analyzed

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Table 1. Gradation of coarse mixes

% Passing

Size (mm) Dense Loose

19.00 100.00 100.0012.50 32.27 32.649.50 32.27 32.644.75 32.27 4.702.36 4.90 4.701.18 4.90 4.700.60 4.90 4.700.30 4.90 4.700.15 4.90 4.700.075 4.90 4.70

Figure 6. Gradation of coarse mixes.

Figure 7. Cutting sections.

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Road Materials and Pavement Design 27

12.5 mm – 2.36 mm – 100 gyrations

12.5 mm – 4.75 mm –100 gyrations

12.5 mm – 4.75 mm – 50 gyrations

Figure 8. Scanned images of coarse mixes.

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Table 2. Analysis of coarse mixtures.

Average Total Per 100 cm2

Mixture No. of Agg No. contacts Total contact length (mm) �c AAAc (deg)

Dense 126.5 130.6 114.5 58.1 63.9Loose -100gy 41.8 44.9 91.6 52.9 63.2Loose-50gy 40.6 37.2 54.0 50.2 61.6Average SkeletonDense 103.3 89.6 86.1 58.6 64.1Loose -100gy 37.4 34.9 64.4 55.1 63.5Loose-50gy 27.0 22.5 32.2 50.6 61.5

Mixture volumetrics

Mixture VMA VTM VFA Gyrations AC% Height (mm)

Dense 14.45 4.42 69.43 100 4.75 120Loose -100gy 17.77 8.74 50.83 100 4.15 145Loose-50gy 21.73 13.14 39.50 50 4.15 150

using the same filtering values and a 2.36 mm minimum aggregate size (i.e., sieve No. 8) forthe dense mix and 4.75 mm (i.e., sieve No. 4) for the loose mixes. The passing sieve No. 200filler was assumed to be part of the mastic, thus it was mixed with the binder prior to mixingwith coarser aggregates (i.e., size 12.5 mm, 4.75 mm, and 2.36 mm). The goal was to capture thedifferences between mixtures’ micromechanical properties using the newly developed internalstructural indices.

A total of eight parameters were chosen to define the aggregate skeleton; (1) the number ofaggregate contacts; (2) total contact length; (3) average absolute angle for normal to contact planeorientation (AAAc); (4) the vector magnitude (as defined by Tashman et al.) for the normal tocontact plane orientations (�c); (5) number of aggregate contacts in skeleton; (6) contact lengthin skeleton; (7) AAAc in the skeleton; and (8) �c in the skeleton. All these indices for eachimage and the average of replicate images for each mix were calculated and the average valuesare shown in Table 2.

The vector magnitude (�c) is an internal structure index, which quantifies the averageanisotropy of contact orientation distribution on a 2-D section image as follows:

�c = 1M

⎡⎣(

M∑i=1

cos 2θi

)2

+(

M∑i=1

sin 2θi

)2⎤⎦

1/2

(1)

where θi is the contact orientation on the 2-D image ranging from −90◦ to 90◦ and M is thetotal number of contacts. Theoretically, the vector magnitude (�c) ranges from 0 as completelyrandom distribution of contacts orientation to 100 (unity) for contacts orientation to be perfectlyaligned in one direction.

The two mixture designs were anticipated to have different properties (i.e., performances). The12.5/2.36 mix was expected to have a better aggregate structure than the 12.5/4.75. Additionally,the 100 gyrations samples were anticipated to have a better aggregate structure than the 50gyrations ones.

Results have indicated a higher number of contacts, longer contact length, higher normal-to-contact angle and vector magnitude, as going from the 12.5/2.36–100 to 12.5/4.75–100 andthen to 12.5/4.75–50. The values for normal to contact plane orientation show that going from

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Road Materials and Pavement Design 29

the 12.5/2.36–100 to 12.5/4.75–100 and to 12.5/4.75–50, the anisotropy of internal structureproperties decreases (i.e. �c values become smaller).

The aggregate skeletons were identified, and the aforementioned parameters were recalcu-lated for the skeleton only. The results show a clearer distinction among the samples when onlyaggregate skeletons were analyzed.

4. Verification of developed indices using regular asphalt mixturesAlthough promising results were found from the validation of the special mixtures with selectedaggregate sizes, regular mixtures used in practice add challenges to the analysis because of theircomplex images containing fine aggregates that are difficult to capture and process. Thus, char-acterization of mixtures with fine aggregate gradations is most challenging, especially whencompared with coarse graded mixtures of the same source.

To verify the capability of the software and the new indices to regular mixes used in practice,a comparison was done between two regular mixes of the same gradation but different binderadditive types, and also mixes of the same binder type but with different gradations. Three mixeswith significantly different mechanical performances in terms of rutting resistance, as measuredby the Flow Number (FN), were chosen for the verification, and the following comparisons ofimage analysis were conducted.

• Coarse graded vs. Fine graded mixture with CBE modified binder (the CBE binder additiveused here is oxidized polyethylene).

• CBE coarse graded vs. Neat coarse graded.

Three images of each sample were analyzed for the color intensity in the gray-scale images.The gray-scale image is an image of mixture, filtered to have color intensity ranging from blackto white. In a gray-scale image, each color has a number ranging from zero for black to 255 forwhite. Asphalt concrete that contains three phases of materials (i.e., aggregate, binder and air)with significantly different color intensities has a pixel intensity histogram with distributions ofintensity, which can define the threshold values for the different phases to be captured.

4.1. CBE coarse vs. CBE fineThe images of these mixes were analyzed using the developed software with a minimum aggregatesize of 1.18 mm since it is the minimum aggregate size captured clearly. The analysis resultsshowed that the CBE coarse mix internal structure was better than the fine mix based on skeletonindices. However, comparing structural indices between mixes of different gradations (fine vs.coarse) alone is insufficient because analyses are performed for a minimum aggregate size of1.18 mm although the percentages of aggregates that are coarser than 1.18 mm are not the samein the two mixes (Figure 9). Therefore, to normalize the results for the same minimum aggregatesize for the fine and coarse mixes, correction factors are needed. Since the indices developed tocharacterize the internal structure of mixtures are related to the surface area of aggregates, thecorrections should be based on specific surface area of aggregates in the mixes.

The method applied to correct the results for coarse and fine mixes uses the aggregate gradationbased on the surface areas of aggregates on each sieve (not weight). To find the surface area ofthe aggregates for each size, according to their weight, the specific area per unit weight is usedand the results are shown in Table 3. Accordingly, area-based gradation for the fine and coarseaggregates are calculated.

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Figure 9. Aggregate gradation for CBE fine and coarse.

Table 3. Calculation of correction factors.

Surface area per%Weight passed unit weight∗ %Surface Area passed Correction factors

Size Coarse Fine Factor Coarse Fine Coarse Fine

19 100.0 100.0 2 100.00 100.00 – –12.5 90.0 100.0 2 98.40 100.00 62.60 –9.5 80.3 89.2 2 97.63 99.55 42.15 224.594.75 46.5 64.9 2 92.23 97.55 12.87 40.842.36 23.7 48.1 4 84.94 94.78 6.64 19.161.18 16.0 33.5 8 80.02 89.97 5.01 9.97

0.6 10.3 24.7 14 73.65 84.89 3.79 6.620.3 7.1 16.6 30 65.98 74.87 2.94 3.980.15 4.0 8.2 60 51.12 54.09 2.05 2.180.075 3.0 3.0 160 38.34 19.79 1.62 1.25

∗ Roberts et al. (1996)

Due to the fact that the number of contacts and contact length is directly dependent on surfacearea, it is assumed that there is a correlation between the specific surface area of aggregates andnumber of contacts and contact lengths. Therefore, the number of contacts and contact lengthsshould be corrected based on the aggregate surface area retained on the sieve No. 16 (1.18 mm)and the total aggregate surface area in the mixes (i.e., extrapolating the indices for all aggregates’sizes based on analysis for aggregates sizes greater than 1.18 mm).

As an example, for the minimum size of 1.18 mm in analysis of coarse graded mix, the per-centage of aggregate weight passing sieve size 1.18 mm is 16%; however, based on surface areafactors per unit weight of aggregates, the surface area of aggregates passing sieve size 1.18 mmis 80.02% of the total aggregate surface area in the mix and the calculated indices are determinedfrom 19.98% of aggregates visible in digital images. Therefore, there is a need to extrapolate theindices value for total mix (i.e., the 100% of surface area of aggregates).

Accordingly, the corrected results and Flow Number values are shown in Table 4.The corrected results show a good correlation with the mechanical response of mixes, which

shows higher number of contact points, contact length, AAAc, and �c for fine graded mix.

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Table 4. Corrected indices for CBE Fine vs. Coarse mixtures and FN

Average Total per 100 cm2 Min agg size = 1.18mm

Total contactMixture No. of Agg No. contacts length (mm) �c AAAc (deg)

F-CBE 7547.0 4958.8 2216.3 53.2 63.6C-CBE 2688.2 2009.3 912.4 48.6 62.7Average SkeletonF-CBE 1682.1 1980.8 1056.9 54.5 64.1C-CBE 1402.2 1139.4 627.9 47.6 62.6

Flow Number

Stress (psi)Sample 150Coarse CBE 210Fine CBE 400

Table 5. Analysis of results for CBE vs. Neat Coarse mixtures and FN.

Average Total per 100 cm2 Min agg size = 1.18mm

Total contactMixture No. of Agg No. contacts length (mm) �c AAAc (deg)

C-Neat 508.5 245.3 82.4 47.9 62.4C-CBE 515.0 384.9 174.8 48.6 62.7Average SkeletonC-Neat 96.1 63.1 26.2 47.7 61.2C-CBE 268.6 218.3 120.3 47.6 62.6

Flow Number

Stress (psi)Sample 150Coarse FH neat 100Coarse CBE 210

A mixture with higher flow number is anticipated to have a higher number of contact points,which makes better connectivity of the structure. Additionally, higher contact length and normal tocontact plane orientation (AAAc) provide more effective aggregate structure. Higher �c indicatesthat most of the contact orientations are in the same direction, which improves the performance.

4.2. CBE coarse vs. neat coarseOne of the most applicable areas for the image processing method is to compare mixtures withthe same gradation that show different performance.

The analyses are done for the coarse graded mixtures with neat binder and CBE modifiedbinder. The results and Flow Number values for each of the mixes are shown in Table 5. Theanalysis of the mixes with the same gradation does not need any correction factors.

The results including the indices calculated in the skeleton (i.e., effective number of contacts,contact orientation and length) have a reasonable correlation with the mechanical performance oftwo mixes.

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5. Application of developed indices to regular asphalt mixturesIn this section, the new indices developed are used for regular mixes that have been produced infield and laboratory studies. Three of the mixes were field samples from the Western CooperativeTest Group (WCTG) project designated as 506, 520 and 523, which have different gradations.The other eight mixes were from the E1B1 task of the Asphalt Research Consortium (ARC)project. Four of these mixtures were fine graded with the same gradation but three differentbinder additives, namely CBE (oxidized polyethylene), SBS (linear styrene-butadiene-styreneblock copolymer) and GTR (ground tire rubber) in addition to the one with Flint Hills (FH) Neatbinder. The other four mixtures were produced with the same binders used for fine mixes but witha coarse graded aggregate blend (same aggregate source). The volumetrics and gradation of eachmix are shown in Tables 6 and 7 and Figure 10. All the samples are compacted with a 7% oftarget air void.

As shown in Figure 10, the gradations of the mixes considered vary significantly. Therefore, asdiscussed before, correction factors are needed for the analysis of these mixes in order to comparethe results.

It is known that changing the aggregate source or type might result in different surface areafactors. Thus, for accurate results, actual surface area factors might need to be determined forthe various mixtures used in the study. However, since this is beyond the scope of this paper, thesurface area factors shown in Table 3 were used for all mixes. Hence, comparing the fine mixes to

Table 6. Volumetric of mixtures.

Mixture VTM %AC VMA VFA

506 7.2 5.60 14.70 73.8520 6.7 5.10 15.20 73.7523 6.5 5.90 16.00 75.0F-CBE 6.6 5.55 13.45 70.3F-SBS 6.7 5.55 13.45 70.3F-GTR 7.3 5.55 13.45 70.3F-Neat 7.0 5.55 13.45 70.3C-CBE 6.4 4.62 14.66 73.0C-SBS 6.4 4.62 14.66 73.0C-GTR 7.0 4.62 14.66 73.0C-Neat 7.0 4.62 14.66 73.0

Table 7. Gradation of mixtures.

% Passing

Size (mm) 506 520 523 Fine Coarse

19 100.0 100.0 100.0 100.0 100.012.5 95.0 91.0 97.0 100.0 90.09.5 86.0 77.0 82.0 89.2 80.34.75 62.0 – 57.0 64.9 46.52.36 47.0 30.0 41.0 48.1 23.71.18 34.0 23.0 25.0 33.5 16.00.6 22.0 19.0 17.0 24.7 10.30.3 14.0 11.0 12.0 16.6 7.10.15 9.0 – 9.0 8.2 4.00.075 5.8 4.4 6.4 3.0 3.0

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Road Materials and Pavement Design 33

Figure 10. Gradation of mixtures.

Table 8. (a) Analysis of mixtures.

Average Total corrected per 100 cm2 Min agg size = 1.18mm

Total contactMixture No. of Agg No. contacts length (mm) �c AAAc (deg)

WCTG -506 7017.7 4222.5 2474.9 54.4 63.6WCTG-520 5523.2 5697.9 3862.5 57.2 65.2WCTG- 523 4856.0 4711.1 2806.7 53.1 63.1F-CBE 7547.0 4958.8 2216.3 53.2 63.6F-SBS 7956.9 5187.8 2191.6 53.5 63.9F-GTR 6713.2 2922.6 1232.2 52.5 63.9F-Neat 6539.2 2368.3 842.8 52.9 63.5C-CBE 2688.2 2009.3 912.4 48.6 62.7C-SBS 3088.9 2373.7 1109.3 50.5 62.2C-GTR 3022.7 1829.6 780.6 48.9 62.3C-Neat 2654.5 1280.5 429.9 47.9 62.4

the coarse mixes of the ARC project is anticipated to be more reliable than between these mixesand the WCTG mixes, since the WCTG mixes have different source of aggregates. The results ofthe analysis are shown in Table 8.

The internal structure indices of the skeleton correlated better with the flow number comparedwith the indices of the total image. The correlations of skeleton indices with flow number areshown in Figure 11.

The results show that there is a strong exponential trend between the number of contacts andcontact length in the skeleton and the flow number. Although the correlations of the normal tocontact plane parameters (AAAc and �c) with the flow number are not as high as the previousindices, they show a fair trend that is reasonable according to their definition.

The analysis shows that the correlation of skeleton internal structure indices with performancein terms of flow number is better than the total indices. This is consistent with the concept ofaggregate skeleton as the effective structure that carries the load.

In order to find out whether each index can sufficiently characterize the differences betweendifferent mixes, an analysis of variance (ANOVA) was conducted for each index in the aggregateskeleton and the P-value was calculated for each as shown in Table 9.

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Table 8. (b) Analysis of mixtures.

Average Skeleton corrected per 100 cm2 Min agg size = 1.18mm

Total contactMixture No. of Agg No. contacts length (mm) �c AAAc (deg)

WCTG-506 1447.0 1762.3 1144.0 56.0 64.5CV% 5.51 17.60 13.01 3.87 0.44WCTG-520 2843.2 4055.2 2910.3 57.2 65.2CV% 7.26 20.13 22.65 0.13 0.63WCTG-523 2186.5 3173.4 2158.8 53.3 63.1CV% 0.98 20.19 39.16 4.83 1.15F-CBE 1682.1 1980.8 1056.9 54.5 64.1CV% 6.46 22.59 20.84 0.85 0.24F-SBS 1701.7 2108.1 1163.3 53.2 63.8CV% 6.23 18.71 23.23 7.00 1.21F-GTR 712.5 808.0 369.8 52.9 63.7CV% 6.56 29.32 28.47 1.92 0.64F-Neat 382.0 391.8 218.1 53.2 61.9CV% 16.40 52.59 49.22 2.32 1.80C-CBE 1402.2 1139.4 627.9 47.6 62.6CV% 3.76 38.92 59.37 10.90 3.64C-SBS 1535.6 1132.7 586.3 50.1 61.6CV% 3.23 8.91 11.21 2.09 0.15C-GTR 963.3 648.4 320.2 52.7 63.3CV% 7.07 32.49 44.87 3.71 0.85C-Neat 501.6 329.5 136.9 47.7 61.2CV% 9.80 34.22 50.10 2.55 1.19

Table 8. (c) Flow number values of mixtures.

Mixture Flow Number (150 Psi)

WCTG-506 340WCTG-520 3400WCTG-523 1275F-CBE 400F-SBS 430F-GTR 240F-Neat 150C-CBE 210C-SBS 260C-GTR 160C-Neat 100

The results of ANOVA show that all the indices in skeleton can capture the difference in internalstructure of mixtures significantly, except the �c. This could be due to the concept that some of theindices can only characterize the mixture performance if combined with others. The interlockingbetween aggregates can be defined better using the combination of contact length and contactorientation for all the contacts, referred to in this study as the Internal Structure Index (ISI), whichis defined as follows:

FN ∝ ISI =N∑

i=1

Resistant component in the direction of the load

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Figure 11. Correlation of different indices calculated for the skeleton with flow number.

ISI =N∑

i=1

contact lengthi ∗ sin(AAci) (2)

where N is the number of contacts in the skeleton and, contact lengthi and AAc i are the contactlength and orientation for the ith contact. The FN values are plotted in Figure 12 versus the ISIvalues calculated for the mixtures used in this study. As shown in Figure 12, a strong correlationbetween ISI and flow number exists, which supports the aforementioned hypothesis

Accordingly, the rutting performance of asphalt mixtures is proportional to the number ofcontact zones, contact length and contact orientation combined together in the skeleton. This

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Table 9. ANOVA for all indices

No. of Aggregates No. contacts contact length �c AAAc

P value 0.0029 0.0001 0.0001 0.1694 0.0347

Figure 12. Correlation of ISI with FN.

concept can be applied to form a prediction model for the rutting performance of asphaltmixtures.

6. Conclusion and recommendationsIn this study, new indices derived from imaging analysis are proposed to characterize asphalt mix-tures’ internal structure. Using these aggregate structure indices, mixtures with different internalstructures could be ranked similar to their mechanical responses to repeated loading as quantifiedby the Flow Number.

The new indices were measured and compared for a set of mixtures with different ruttingperformance. The results show that there is a strong correlation between the internal structureindices and the rutting performance of mixtures. It was found that the correlation using theindices measured in the skeleton was better.

The results of this study also showed that binder modification and additives affect the internalstructure of asphalt mixtures for the same gradations due to the rheological properties of modifiedbinders.

The iPas software, with the modification achieved in this study, has shown to be a very promisingtechnique as a simple procedure to capture and characterize the internal structure of 2-D imagesof asphalt mixtures using indices that correlated well with rutting mechanical performance (i.e.,Flow Number).

It should be noted that the analyses conducted in this research are based on the images ofmixtures before loading, which represents the inherent structure of the asphalt mixtures. As themixture is subjected to loading, the internal structure changes, which is referred to as inducedmicrostructure. The topic of induced properties of asphalt mixture internal structure is an area ofresearch that needs to be explored in the future.

AcknowledgementsThis research was sponsored by the Asphalt Research Consortium (ARC), which is managed by FHWA andWRI. This support is gratefully acknowledged. The results and opinions presented are those of the authors

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and do not necessarily reflect those of the sponsoring agencies. The authors would also like to thank Dr RaulVelasquez and Aaron Coenen for their contributions to this paper.

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