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36 influence and alter the measurements of angularity and sizes. The third method was to group the analysis results texture. obtained for each characteristic using data from all operators While the X-ray CT is a powerful research tool, it is pre- and for all sizes combined. Results of clustering using the mature to use it as a practical tool for routine measurements three different methods are shown in Figure 12. Figure 12a of aggregate shape. shows the groups' limits of the coarse aggregate texture for each size, the average for the limits of three sizes ("Avg. Sizes" label in Figure 12a), and for all sizes combined ("All" label in Statistical-Based Methodology for Figure 12a). The results show that the groups' limits obtained Classification of Aggregates using the three were very close. The same conclusion was The ease of interpretation of test results is an essential part reached by examining the results in Figures 12b and 12d for to facilitate the implementation in practice. The imaging-based the other characteristics. tests discussed in this report provide measurements of a large Further analysis was also conducted to determine whether it is feasible to unify the angularity groups' limits of both the number of particles. These measurements are valuable to detect fine and coarse fractions. The groups' limits for the angular- differences between aggregates based on sound statistical ity of fine and coarse aggregates were determined, plotted in methods. Therefore, it is essential to develop a methodology Figure 12, showing slight differences between the limits of to summarize the measurements and present them to the user fine and coarse fractions, with the largest difference being in in a simple form that facilitates implementation. the third group. This difference, however, is small compared This section contains a methodology presented in the visual to the actual angularity values, and thus could be unified basic program of an Excel workbook to summarize the aggre- limits. The new aggregate shape classification limits are shown gate characteristics and classify aggregates based on these char- in Figure 13. acteristics. The program includes graphical presentations of the results, helps to compare the results from different aggregates, and combines the results of multiple analyses of the same Analysis and Results aggregate source. The AIMS software was used to calculate the percentages Aggregates' shape, angularity, and texture were measured of each aggregate that belong to the different groups in Fig- using the three analysis methods that are part of the AIMS ure 13; the results are shown in Figures 14 and 15. These software: (1) sphericity as a 3-D measure of coarse aggregates, figures show the distribution of a certain shape property in (2) gradient angularity for coarse and fine aggregates, and a number of aggregate samples. The variability in the char- (3) texture of coarse aggregates quantified by the wavelet acteristics within and between aggregates indicates that com- method. Measurements from 195 tests on coarse aggregates paring or classifying aggregates based on percent of particles and 75 tests on fine aggregates were used in developing the in a single group could be misleading. This is also true for the methodology. On average, a coarse aggregate test involved classification based on average values, especially when an 56 particles and a fine aggregate test involved about 300 par- aggregate sample includes a small percent of particles that ticles. All these data were used in the development of the have extremely high or low values. As such, the new classifi- new classification system. The use of different operators cation methodology considers the distribution rather than and repeated measurements ensured that the classification an average value. The discussion provided in the following methodology accounted for variations in measurements sections highlights the implications of using the developed among operators. methodology on aggregate shape classification with emphasis Cluster analysis was used to develop groups (or clusters) on examining the effects of different factors such as crushing of aggregates based on the distribution of their characteris- on aggregate characteristics. tics. In this study, the usual metric of Euclidean distance (Equation 9) and Ward's Linkage method were used. The clus- Aggregate Texture versus Angularity tering method was applied to all characteristics obtained from AIMS. The classification methodology incorporates measurements Three methods for grouping the analysis results were used of texture and angularity for coarse aggregates, but it uses with the objective of determining whether common group angularity measurements only for fine aggregates. A study by limits can be obtained for aggregates irrespective of their size. Masad et al. (24) clearly showed that a high correlation exists In one method, group limits were selected for each aggregate between angularity (measured on black and white images) characteristic based on measurements by all operators for and texture (measured on gray-scale images) of fine aggregates. each size separately. In another method, the group limits This finding led to focusing on fine aggregates angularity mea- were determined by averaging those obtained for the three sured on black and white images. This is an easier task than

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37 900 All 800 Avg Sizes 3/4 700 3/8 600 #4 Texture Index 500 400 300 200 100 0 Polished Smooth Low Roughness Moderate High Roughness Roughness Upper Limits for Texture Classes (a) Coarse Aggregates Texture 12000 All Avg Sizes 10000 3/4 3/8 #4 8000 Gradient Angularity 6000 4000 2000 0 Rounded Sub-Rounded Sub-Angular Angular Upper Limits for Gradient Angularity Classes (b) Coarse Aggregates Angularity Figure 12. Limits of groups (clusters) of individual and combined aggregates. (continued on next page)

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38 12000 All Avg Sizes 10000 #8 #16 #60 Gradient Angularity 8000 6000 4000 2000 0 Rounded Sub-Rounded Sub-Angular Angular Upper Limits for Gradient Angularity Classes (c) Fine Aggregates Angularity All 1 Avg Sizes 3/4 3/8 0.8 #4 Sphericity 0.6 0.4 0.2 0 Flat Elongated Low Sphericity Moderate Sphericity High Sphericity Upper Limits for Sphericity Classes (d) Coarse Aggregates Shape (Sphericity) Figure 12. (Continued).

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12000 Coarse-ALL Fine-ALL 10000 Coarse-Avg Fine-Avg 8000 Gradient Angularity 6000 4000 2000 0 Rounded Sub-Rounded Sub-Angular Angular Upper Limits for Gradient Angularity Classes (e) Coarse and Fine Aggregates Angularity Figure 12. (Continued). Angularity 2100 4000 5400 Rounded Rounded Angular Angular Sub Sub Shape 0.6 0.7 0.8 1.0 Elongated Sphericity Sphericity Sphericity Moderate High Flat/ Low Texture 165 275 350 460 Roughness Roughness Roughness Moderate Polished Smooth High Low Figure 13. Aggregate characteristics classification chart.

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40 100% Percentage in Texture Group 80% (a) Texture in Coarse Aggregate 60% 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 Aggregate Label Polished Smooth Low Roughness Moderate Roughness High Roughness 100% Percentage in Angularity Group 80% (b) Angularity in 60% Coarse Aggregate 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 Aggregate Label Rounded Sub Rounded Sub Angular Angular 100% 80% Percentage in Form Group (c) Form in Coarse 60% Aggregate 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 Aggregate Label Flat Elongated Low Sphericity Moderate Sphericity High Sphericity Figure 14. Distributions of coarse aggregate characteristics.

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41 100% 90% Percentage in Angularity Group 80% 70% 60% 50% 40% 30% 20% 10% 0% 1 2 5 6 10 Aggregate Label Rounded Sub-Rounded Sub-Angular Angular Figure 15. Distributions of fine aggregate angularity. capturing the surface texture of fine aggregates rapidly and Figure 18. Aggregate size did not have a noticeable influence accurately using a computer-automated system. In the case of on texture. However, aggregate angularity changed with aggre- coarse aggregates, it was found that there is a distinct differ- gate size. ence between angularity and texture, and these two properties The analysis methods also captured the influence of crush- have different effects on performance (24, 25). As can be seen ing on shape or proportions of particle dimensions. The effect from Figure 16a, which shows the average texture and corre- of aggregate size on sphericity varied from one aggregate to sponding angularity for each of the coarse aggregate samples, another. For example, the sphericity of the crushed river gravel aggregates could have high angularity but low texture. This is was higher than for uncrushed gravel, indicating that aggregate even true for individual particles, as shown in Figure 16b. crushing made the particles more equi-dimensional. How- Particles from aggregates CA-2 and CA-9 (see Table 4) had ever, the crushed glacial gravel (CA-7) showed less sphericity comparable angularity values but there was a significant dif- than the uncrushed material (CA-8). ference in texture. Crushing the natural sand FA-1 to become FA-2 increased The cumulative distribution of texture in the coarse aggre- angularity, as depicted in Figure 15. FA-1 is an example of high gate samples shown in Figure 17 indicates that the texture of quality natural sand that had angularity comparable to some these aggregate samples was spread over a wide range; none of manufactured sands. For example, FA-1 had higher angularity the other characteristics had such a wide range. Texture also had than crushed limestone (FA-6). higher variability than angularity within an aggregate sample Shown in Figure 19 is an example of the effect of size on (see Figure 14a and Figure 14b). fine aggregate angularity. Angularity increased as particle size decreased due to crushing. Effect of Crushing and Size on Shape Properties The developed methodology can be used to examine the Identifying Flat, Elongated, or Flat and Elongated Particles influence of crushing on shape. Two types of crushed and uncrushed aggregates were used in this study: river gravel The sphericity value gives a very good indication of the (CA-1 and CA-2) and glacial gravel (CA-7 and CA-8). CA-1 proportions of particle dimensions. However, one cannot and CA-8 were uncrushed, while CA-2 and CA-7 were crushed. determine whether an aggregate has flat, elongated, or flat and The results in Figures 14a and 14b show that crushing the elongated particles using the sphericity alone. To this end, the gravel did not influence texture, but significantly increased chart shown in Figure 20 is included in the AIMS software to their angularity. distinguish among flat, elongated, and flat and elongated par- Texture measurements were conducted on different sizes ticles. Superimposed on this chart are the 3:1 and 5:1 limits of the same aggregate type in order to investigate the influence for the longest to shortest dimension ratio and the results from of aggregate size on texture. Examples of results are shown in CA-2 and CA-4. The figure shows that both aggregates pass

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42 4000 CA-5 3500 CA-7 CA-13 CA-9 3000 CA-2 Gradient Angularity Index CA-3 CA-11 CA-12 CA-4 CA-10 2500 CA-6 CA-8 2000 CA-1 1500 1000 500 0 0 100 200 300 400 500 600 Texture Index (a) Average Texture and Angularity of Coarse Aggregates 10000 CA-2 9000 CA-9 8000 Gradient Angularity Index 7000 6000 5000 4000 3000 2000 1000 0 0 100 200 300 400 500 600 700 800 900 Texture Index (b) Texture and Angularity of Coarse Aggregate Particles Figure 16. Variations in texture and angularity properties in coarse aggregates.

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43 CA-1 CA-2 Low Moderate CA-3 Polished Smooth Roughness Roughness High Roughness 100 CA-4 90 CA-5 80 Percentage of Particles, % CA-6 70 CA-7 60 CA-8 50 CA-9 40 CA-10 30 CA-11 20 CA-12 10 CA-13 0 0 200 400 600 800 Texture Index Figure 17. Texture index for different coarse aggregate types. CA-1-3/4 CA-1-3/8 Low Moderate Polished Smooth Roughness Roughness High Roughness 100 CA-1-#4 90 CA-7-3/4 80 Percentage of Particles, % 70 CA-7-3/8 60 CA-7-#4 50 40 CA-10-3/4 30 20 CA-10-3/8 10 CA-10-#4 0 0 200 400 600 800 Texture Index Figure 18. Examples of the effect of coarse aggregate size on texture.

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44 Sub- Sub- Angular FA-2-#8 Rounded Rounded Angular 100 90 80 Percentage of Particles, % 70 FA-2-#16 60 50 40 30 20 FA-2-#60 10 0 0 1000 2000 3000 4000 5000 6000 7000 8000 Angularity "Gradient Method" Figure 19. Example of the effect of fine aggregate size on angularity. Ratio of shortest to longest axes 1:5 1:3 1 Particles Become Less Elongated 0.9 Intermediate/Long = Elongation Ratio 0.8 0.7 SP=0.8 CA-2 0.6 SP=0.7 0.5 SP=0.6 CA-4 0.4 SP=0.5 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Short/Intermediate = Flatness Ratio Particles Become Less Flat Figure 20. Chart for identifying flat, elongated, or flat and elongated aggregates. the 5:1 requirement (both had less than 10 percent particles have been obtained if aggregates were classified based on the with dimensional ratio of 5:1), but have distinct distributions in ratio of 5:1 only. This information will help to understand the terms of flat and elongated particles. Such analysis reveals influence of aggregate characteristics on asphalt and concrete valuable information about the distribution that would not mix properties.