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Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements (2006)

Chapter: Chapter 3 - Interpretation, Appraisal, and Application

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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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Suggested Citation:"Chapter 3 - Interpretation, Appraisal, and Application." National Academies of Sciences, Engineering, and Medicine. 2006. Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements. Washington, DC: The National Academies Press. doi: 10.17226/13977.
×
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24 The primary objective of NCHRP Project 4-19(2) was to validate performance-related aggregate tests recommended by Kandhal and Parker (1). A discussion of the relationships of these aggregate test methods to mixture performance and aggregate properties follows. Coarse-Graded Mixtures Kandhal and Parker recommended the coarse aggregate UVA (AASHTO TP 56) and the FOE21 as the first and second best coarse aggregate tests related to rutting, respectively. They also found that FOE51 was an important variable, but it was not recommended because of the narrow range in results and did not provide a clear measure of a coarse aggregate’s performance. The regression equations developed by Kandhal and Parker (1) suggested that as UVA increases, both rutting and rate of rutting decrease. An increase in FOE21 causes a reduction of mixture stiffness and an increase in the rate of rutting. In summary, Kandhal and Parker (1) suggested that high coarse aggregate UVA and low FOE21 values are desir- able for improved pavement performance. The current research also found a strong correlation between FOE21 and coarse aggregate UVA (r = 0.786, p-value = 0.064). The cor- relation suggests that the FOE21 value significantly influ- ences the orientation and structure of coarse aggregate as reflected by the UVA values. Because the two variables, coarse aggregate UVA and FOE21, are highly correlated, it is not appropriate to use both as independent variables in a statistical model. Moreover, the conclusion drawn by Kandhal and Parker (1) about the effect of FOE21 on pavement permanent deformation can become unrealistic. In fact, a high percentage of FOE21 aggregate par- ticles are desirable for HMA pavements. For the current study, a correlation matrix was developed for eight of the coarse aggregate tests and three rutting parameters. The total rut depth at 20,000 wheel passes was not included because the CA-3 (Uncrushed Gravel) mixture has no data at that point. The matrix is shown in Table 20. The correlations between the rutting parameters and the coarse aggregate UVA and UVB are significant. Each of the test methods was assigned a descriptive ranking as shown in Table 20. The rank- ing was determined by assigning a value from 0 to 9 depending on the correlation coefficient between the given test method and the rutting parameters. For example, if the correlation coeffi- cient for a given test was higher than 0.80, but less than 0.90, the descriptive ranking was assigned as 8. The ranking for a given test method was then averaged for the rutting parameters used. Thus the higher the ranking number, the better the aggregate test relates to HMA rutting performance in the APT. Table 20 shows that the coarse aggregate UVA and UVB have the highest descriptive rankings, 9.0, while the FOE21 has the next closest at 8.0.The remaining aggregate tests have much lower rankings. Plots of the three permanent deformation parameters and the coarse aggregate UVA values are shown in Figures 16 through 18. These figures suggest that higher coarse aggregate UVA values result in greater resistance to perma- nent deformation. The plots also indicate that continued increase in rut resistance becomes negligible for UVA values higher than approximately 50 percent. Although the flat or elongated and flat and elongated values at the 3:1 ratio (FOE31and F&E31) were expected to correlate positively with FOE21, the data shown in Table 20 indicated that neither of the 3:1 ratios correlates well to the rutting parameters. The relationships between rutting and FOE31 or F&E31 suggest that factors other than particle shape contribute to explaining rut resistance. Coarse aggregate UVA appears to successfully capture the effect of particle shape, surface charac- teristics, or mineralogy on rutting performance of the HMA mixtures tested in this research. To further investigate interaction of aggregate tests, multiple regression analyses were performed between rutting parame- ters and coarse aggregate test data. The response variables included total rut depth at 5,000 wheel passes and total rut rate C H A P T E R 3 Interpretation, Appraisal, and Application

25 The relationships between coarse aggregate UVA and APT traffic to reach 3.5- and 7.0-mm rut depths appear to be expo- nential and significant. The regression equation shown in Fig- ure 19 indicates the number of APT wheel passes to reach a 3.5-mm total rut depth increases by approximately 53 passes for an increase in coarse aggregate UVA from 45 to 50 percent. Figure 20 shows the number of wheel passes increases by approximately 3,715 to reach a 7.0-mm total rut depth for the same 5 percent increase in coarse aggregate UVA. The expo- nential relationship would seem logical up to a point. For example, for coarse aggregate UVA values between 35 and 40 percent, the number of wheel passes to reach a given rut depth increases very little. However, the increase is significant for coarse aggregate UVA values between 45 and 50 percent. In practice, most crushed aggregates appear to have coarse aggregate UVA values less than approximately 50 percent. Figure 21 presents the APT rut depths as a function of the number of wheel passes for each of the coarse aggregates used in the study. The coarse aggregate UVA values are somewhat Figure 16. Rut depth/coarse aaggregate UVA relationship. y = -2.76x + 144.11 R2 = 0.90 0 5 10 15 20 25 30 35 40 42 44 46 48 50 52 UVA, % To ta l R ut D ep th @ 50 00 W he el P as se s, m m y = -1.54x + 78.59 R2 = 0.89 0 5 10 15 40 45 50 55 UVA, % La te r R ut R at e, m m /lo g(N ) Figure 18. Late rut rate/coarse aggregate UVA relationship. Rutting ParameterCoarse Aggregate Property Total Rut Depth @ 5000 Passes Early Rut Rate Later Rut Rate Descriptive Ranking UVA -0.9470.015 -0.963 0.008 -0.945 0.015 9.0 UVB -0.9830.003 -0.995 0.0004 -0.983 0.003 9.0 LALOSS -0.1610.796 -0.242 0.695 -0.151 0.809 1.0 MDEV 0.3640.547 0.291 0.635 0.372 0.537 2.7 FOE21 -0.8260.086 -0.845 0.071 -0.819 0.090 8.0 FOE31 -0.3600.552 -0.436 0.463 -0.351 0.563 3.3 F&E31 -0.4760.418 -0.534 0.354 -0.467 0.428 4.3 F&E51 -0.4680.426 -0.550 0.337 -0.462 0.434 4.3 Table 20. Correlation matrix of coarse aggre- gate properties and rutting parameter. Figure 17. Early rut rate/coarse aggregate UVA relationship. y = -0.40x + 21.52 R2 = 0.93 0 1 2 3 4 5 40 45 50 55 UVA, % Ea rly R ut R at e, m m /lo g(N ) for both early and late traffic. The results, shown in Table 21, indicate that the coarse aggregate UVA is a good indicator of HMA mixture rutting performance. When the results from other aggregate properties such as FOE21 were combined with UVA, the R2 does not change significantly. This finding indi- cates that the coarse aggregate UVA alone is a sufficient predic- tor of HMA mixture rutting performance. However, it should be recognized that these data were developed for coarse-graded mixtures using a few coarse aggregate types. To determine sensitivity of coarse aggregate UVA to traffic levels, the effect of coarse aggregate UVA on the number of APT wheel passes to reach total rut depths of 3.5 and 7.0 mm was examined; results are shown in Figures 19 and 20, respec- tively. The 7.0-mm total rut depth was selected because it was the minimum total rut depth exhibited at 20,000 wheel passes during the APT tests; the 3.5-mm total rut was arbitrarily chosen as one-half of the 7.0-mm rut depth.

26 grouped. The coarse aggregates with UVA values of 49 and 51 percent resulted in a rut depth of approximately 6 mm at 20,000 APT wheel passes; the two coarse aggregates with UVA values of 48 percent resulted in a total rut depth of about 10 mm after 20,000 passes; and the rounded gravel with a coarse aggregate UVA of 42 percent resulted in a rut of 30 mm at 5,000 wheel passes. These data suggest that a minimum coarse aggregate UVA value of 45 percent would be desirable. Fine-Graded Mixtures In NCHRP Report 405, correlations between fine aggregate properties and SST results were found to be poor; only the GLWT results were used for recommending fine aggregate UVA as a predictor of mixture rutting performance. In the current study, HMA aggregates were tested using ASTM C 1252, Methods A and B, as well as VTM5. These three tests are significantly correlated and give similar correlations with APT permanent deformation results. Rutting parameters used to evaluate fine-graded HMA mixtures’ rutting potential were rut depths at 1,000; 5,000; and 20,000 APT wheel passes, and early and late traffic rut- ting rates. Correlation analyses were conducted between the rutting parameters and the fine aggregate test results. In addi- tion to the UVA, UVB, and VTM5 tests, other aggregate tests incorporated in the analysis were Micro-Deval (MDEV); magnesium sulfate soundness (MGSO4); and D60, D30, and y = 0.0065e0.19x R2 = 0.45 0 50 100 150 40 45 50 55 UVA, % N um be r o f A PT W he el P as se s to R ea ch 3 .5 m m T ot al R ut Figure 19. Effect of coarse aggre- gate UVA on traffic (3.5 mm). y = 10-9e0.58x R2 = 0.80 0 5000 10000 15000 20000 40 45 50 55 UVA, % N um be r o f W he el P as se s t o Re ac h 7 m m To ta l R ut Figure 20. Effect of coarse aggre- gate UVA on traffic (7 mm). 0 5 10 15 20 25 30 35 0 5000 10000 15000 20000 Number of Wheel Passes, N R ut D ep th , m m UVA=42, N7=50 UVA=48, N7=858 UVA=48, N7=897 UVA=49, N7=13,370 UVA=51, N7=6210 Figure 21. Effect of coarse aggregate UVA on rut depth. Response Variable PredictorVariable Regression Equation R 2 p-value Total Rut Depth at 5,000 Wheel Passes UVA 164.54 – 3.19 UVA 0.96 0.004 Early Rut Rate UVA 23.6277 – 0.44 UVA 0.95 0.005 Later Rut Rate UVA 90.14 – 1.78 UVA 0.96 0.004 Table 21. Regression analyses between rutting parameters and coarse aggregate UVA.

D10 of the p0.075 fraction. The results are shown in Table 22. Only UVA, UVB, and VTM5 produce consistent correlations with the rutting parameters, although the MDEV and MGSO4 show good correlations with rut depth at 20,000 wheel passes. Plots of the rutting parameters as a function of fine aggregate UVA are shown in Figures 22 through 26. Similar to the procedure used for the coarse-graded mix- tures, each of the fine aggregate test methods was assigned a descriptive ranking as shown in Table 22. The ranking was determined in the manner described for the coarse-graded mixtures. Table 22 shows that fine aggregate UVB and VTM5 have the highest descriptive rankings at 7.0; the fine aggregate UVA is only slightly less at 6.4. The D60, D30, and MDEV rankings are the next closest at 5.6, 5.2, and 5.0, respectively. Regression analyses were performed to develop equations relating rutting parameters and aggregate test results. The pri- mary independent variables were UVA, UVB, and VTM5. The response variables were rut depths at 1,000; 5,000; and 20,000 APT wheel passes; and early and late traffic rutting rates. y = -0.73x + 44.44 R2 = 0.28 0 5 10 15 20 40 42 44 46 48 50 UVA, % To ta l R ut D ep th @ 5 00 0 W he el P as se s, m m y = -1.03x + 60.32 R2 = 0.46 0 5 10 15 20 25 30 35 40 42 44 46 48 50 UVA, % To ta l R ut D ep th @ 2 00 00 W he el Pa ss es , m m Figure 23. Rut depth/fine aggregate UVA relationship (5,000 passes). Figure 24. Rut depth/fine aggregate UVA relationship (20,000 passes). UVA UVB VTM5 MDEV MGSO4 D60 D30 D10 1,000 passes -0.809 0.051 -0.847 0.033 -0.838 0.037 -0.393 0.441 -0.393 0.441 0.736 0.096 0.773 0.071 0.393 0.441 5,000 passes -0.372 0.538 -0.511 0.379 -0.517 0.372 0.765 0.132 0.272 0.659 0.315 0.606 0.292 0.634 0.412 0.490 To ta l r ut d ep th 20,000 passes -0.715 0.286 -0.666 0.334 -0.685 0.315 0.948 0.052 0.906 0.094 0.466 0.534 0.312 0.688 0.360 0.641 Early Rut Rate -0.793 0.0599 -0.846 0.034 -0.839 0.037 -0.287 0.581 -0.354 0.492 0.727 0.102 0.762 0.078 0.326 0.529 Later Rut Rate -0.795 0.059 -0.822 0.045 -0.812 0.0499 -0.483 0.332 -0.419 0.409 0.746 0.088 0.785 0.064 0.459 0.360 Descriptive Ranking 6.4 7.0 7.0 5.0 4.2 5.6 5.2 3.4 Table 22. Correlation matrix between fine aggregate prop- erties and rutting parameters. y = -2.08x + 106.21 R2 = 0.62 0 5 10 15 20 25 30 35 40 42 44 46 48 50 UVA, % To ta l R ut D ep th @ 1 00 0 W he el P as se s, m m Figure 22. Rut depth/fine aggregate UVA relationship (1,000 passes). 27

28 Results of the regression analyses are given in Table 23. The relationships between total rut depth at 1,000 wheel passes and early and late traffic rutting rates and UVA, UVB, or VTM5 are good/fair and significant. These are the three rutting parame- ters that made use of all six fine aggregates. Not all six fine aggregates were used for rut depth analysis because no rut depth data were obtained for the FA-1 mixture at 5,000 and 20,000 APT wheel passes and for the FA-2 mixture at 20,000 wheel passes. Additional fine aggregate properties such as MDEV and MGSO4 were included in the regression analyses using stepwise regression, but these additions failed to improve the R2 values. These results show that as fine aggre- gate UVA, UVB, or VTM5 values increase, the rutting resist- ance of an HMA mixture also increases; however, UVA may be preferable to UVB or VTM5 because the UVA test requires less material and time. Total rut depths at 5,000 and 20,000 wheel passes have poor correlations with fine aggregate UVA, UVB, and VTM5 (see Table 23). Although the correlations are poor, the plots indi- cate the effect of fine aggregate UVA, UVB, and VTM5 on rut- ting performance. Close examination suggests increasing fine aggregate UVA from 42 to approximately 47 percent does not appear to improve HMA rutting resistance; however, rutting resistance increases significantly for fine aggregate UVA values above 47 percent. Kandhal and Parker (1) reported similar results. Based on their GLWT data, mixture rutting suscepti- bility does not change much for fine aggregate UVA values of approximately 45 to 46 percent, but mixtures become less sus- ceptible to rutting for fine aggregate UVA values above 46 per- cent. Furthermore, at UVA values of around 39 to 40 percent, HMA mixtures tested in both the GLWT and APT exhibited significant increases in rut depths. Based on results in this research and those reported by Kandhal and Parker, it appears that fine aggregate UVA is a good predictor variable of HMA rutting performance. How- ever, it should be recognized that in-place properties of HMA pavements, such as pavement density, also affect pavement performance. Furthermore, it is recommended that natural sands with fine aggregate UVA values below 40 percent be avoided, unless they are combined with a higher UVA fine y = -0.41x + 22.38 R2 = 0.62 0 2 4 6 8 10 40 42 44 46 48 50 UVA, % Ea rly R ut R at e m m /lo g(N ) y = -1.49x + 74.16 R2 = 0.58 0 5 10 15 20 25 40 42 44 46 48 50 UVA, % La te R ut R at e m m /lo g(N ) Figure 26. Late rut rate/fine aggregate UVA relationship. Figure 25. Early rut rate/fine aggre- gate UVA relationship. Response Variable Predictor Variable Model Equation R2 p-value UVA 100.26 – 1.95 UVA 0.65 0.051 UVB 112.98 – 2.05 UVB 0.72 0.033 Rut Depth @ 1,000 passes1 VTM5 108.88 – 1.92 VTM5 0.70 0.037 UVA 34.52 – 0.52 UVA 0.14 0.538 UVB 47.88 – 0.74 UVB 0.26 0.379 Rut Depth @ 5,000 passes2 VTM5 46.34 – 0.69 VTM5 0.27 0.372 UVA 63.67 – 1.11 UVA 0.51 0.286 UVB 66.29 – 1.06 UVB 0.44 0.334 Rut depth @ 20,000 passes3 VTM5 64.84 – 1.01 VTM5 0.47 0.315 UVA 22.29 – 0.42 UVA 0.63 0.060 UVB 25.40 – 0.45 UVB 0.72 0.034 Early Rut Rate1 VTM5 24.56 – 0.42 VTM5 0.70 0.037 UVA 70.66 – 1.41 UVA 0.63 0.059 UVB 78.86 – 1.46 UVB 0.68 0.045Later RutRate1 VTM5 75.82 – 1.36 VTM5 0.66 0.0499 1All mixtures are included 2FA-1 (Natural Sand A) is not included 3FA-1 (Natural Sand A) and FA-2 (Crushed Gravel Sand) are not included Table 23. Regression analyses between rutting parameters and fine aggregate UVA.

aggregate and/or pass a performance test. Fine-graded HMA mixtures with fine aggregates having UVA values between 42 and 47 percent tend to exhibit similar rutting performance. Mixtures with fine aggregate UVA values above 47 percent appear to exhibit better rutting performance. However, expe- rience has shown that an HMA mixture with a high fine aggregate UVA can produce a mixture with high voids in the mineral aggregate (VMA). Such mixtures require a high binder content to meet air voids criteria, which can lead to over-asphalting and poor mixture rutting performance. Sensitivity of the fine aggregate UVA to APT traffic levels was analyzed at the 7.5-mm and 10-mm total rut depths. Figures 27 and 28 show the relationships between fine aggre- gate UVA and the number of APT wheel passes required to reach a 7.5- and 10-mm total rut depth, respectively. Both rela- tionships appear to be exponential and significant. The expo- nential regression equation shown in Figure 27 indicates the number of APT wheel passes required to reach a 7.5-mm total rut depth increases by approximately 950 for an increase in fine aggregate UVA from 45 to 50 percent. For this same 5-percent increase in fine aggregate UVA, the equation shows the num- ber of APT wheel passes increases by approximately 6,600 to reach a 10-mm total rut depth. These relationships seem log- ical up to a point. For example, for fine aggregate UVA values between 35 and 40 percent, the number of wheel passes applied before reaching a given rut depth increases very little. However, the increase is much larger for fine aggregate UVA values between 45 and 50 percent. The practical limit to the relationship is that many fine aggregates have UVA values of no more than approximately 50 percent. Figure 29 presents the APT rut depths as a function of the number of wheel passes for each of the fine aggregates used in the study. Unlike the coarse aggregate results, the fine aggregate UVA values are not grouped. Two of the fine aggregates have UVA values of 49 percent, but the gran- ite outperformed the traprock; the former yielding approx- imately a 5-mm rut depth at 20,000 wheel passes and the latter an 11-mm rut depth at 20,000 wheel passes. Natural Sand B with a UVA of 42 percent performed as well as the dolomite sand with a UVA of 47 percent; both exhibited 0 5 10 15 20 25 30 35 0 5000 10000 15000 20000 Number of Wheel Passes, N R ut D ep th , m m UVA=40, N10=75 UVA=46, N10=500 UVA=42, N10=2500 UVA=49, N10>20,000 UVA=47, N10=2500 UVA=49, N10=7500 Figure 29. Effect of fine aggregate UVA on rut depth. y = 8E-05e0.33x R2 = 0.57 0 500 1000 1500 2000 2500 40 42 44 46 48 50 UVA, % N um be r o f A PT W he el P as se s to R ea ch 7 .5 m m T ot al R ut y = 0.0002e0.35x R2 = 0.51 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 40 42 44 46 48 50 UVA, % N um be r o f A PT W he el P as se s to R ea ch 1 0 m m T ot al R ut Figure 28. Effect of fine aggregate UVA on traffic (10 mm). Figure 27. Effect of fine aggregate UVA on traffic (7.5 mm). 29

approximately a 15-mm rut depth at 20,000 wheel passes. Natural Sand A is the poorest fine aggregate-yielding in excess of 30 mm of rutting after approximately 1,000 APT wheel passes. Moisture Susceptibility Mixtures In NCHRP Report 405, Kandhal and Parker recommended the methylene blue value of the p0.075 fraction of the fine aggregates as the best predictor for the stripping potential of fine-graded HMA pavements. They used AASHTO T 283 and the GLWT tracking device to investigate the influence of fines in fine aggregate on stripping. The GLWT test was conducted under water maintained at 50°C and an inflection point on a plot of rutting versus number of passes was used as the param- eter for evaluating mixture stripping performance. In the cur- rent study, only mixture FAM5 (Natural Sand B) had an obvious inflection point, which was presumably caused by stripping initiation. All other mixtures tested did not exhibit an obvious inflection point. Mixture FAM3 (Granite Sand) exhibited a slight change in slope that is possibly an indication of stripping. Methylene Blue Test The tensile strength ratio (TSR) determined in the AASHTO T 283 test was used to study the effect of the fine aggregate on the moisture susceptibility of the HMA mix- tures. Kandhal and Parker (1) suggested that the moisture susceptibility of fine-graded HMA mixtures was related to the MBV of fine aggregates. The goal of the current study was to validate the effect of fine aggregate properties on HMA mois- ture susceptibility. The correlation between TSR and the MBV of the fine aggregates was investigated, but no significant correlation was found. A plot of TSR as a function of MBV is shown in Figure 30. The TSR value of 100 percent for the FAM1 (Natural Sand A) mixture seems out of place given the MBV of 3.3. Further investigation revealed that the FAM1 mixture also had the least p0.075 material (3.0 percent). This amount may be low enough that the poor quality of fines did not affect its tensile strength; however, the figure suggests some trend in the data. Given the variability of the AASHTO T 283 test results, a clear relationship between HMA moisture susceptibility as meas- ured by the test and the MBV test is not readily apparent. Rutting Given that only one mixture exhibited an obvious inflection point, rutting parameters were also used to evaluate the effect of moisture on HMA performance. Parameters evaluated included rut depths at 1,000 and 5,000 APT wheel passes and 30 rutting rates for both early and late traffic. The results are shown in Table 24. Rutting data for mixture FAM1 (Natural Sand A) was not available for 5,000 wheel passes. It can be seen that none of the correlations are significant at a level of 5 per- cent. Data from fine aggregate UVA, UVB, and VTM5 tests have good correlations with the rutting parameters, but again are not significant. The MBV values have poor/fair positive correlations with the rutting parameters. Particle size param- eters have poor correlations with the rutting parameters. The relationships between each of these rutting parameters and fine aggregate UVA are shown in Figures 31 through 34. A descriptive ranking was assigned to each test method as shown in Table 24. The table shows that fine aggregate UVA has the highest ranking of 8.3 followed closely by fine aggre- gate UVB and VTM5 tests at 8.0. The MBV has a ranking of 4.5 and the remaining tests have rankings of 3 or less. The rankings would seem to confirm that the fine aggregate UVA is the best test for predicting HMA mixture performance. Regression analyses were performed to determine if com- binations of aggregate test variables would provide an improved relationship with rutting parameters. The inde- pendent variables were fine aggregate UVA, UVB, and VTM5; y = -1.60x + 94.46 R2 = 0.23 50 60 70 80 90 100 0 2 4 6 8 10 MBV Te ns ile S tre ng th R at io , % Figure 30. TSR/MBV relationship. Table 24. Correlation matrix for rutting parameters and fine aggregate properties (moisture susceptibility tests). UVA UVB VTM5 MBV D60 D30 D10 Rut depth at 1,000 Passes -0.858 0.063 -0.856 0.064 -0.847 0.070 0.555 0.332 -0.293 0.632 -0.235 0.704 -0.462 0.434 Rut depth at 5,000 Passes -0.948 0.052 -0.860 0.140 -0.858 0.142 0.257 0.743 -0.601 0.399 -0.453 0.547 -0.281 0.719 Early Rut Rate -0.867 0.057 -0.842 0.073 -0.834 0.079 0.666 0.220 -0.308 0.614 -0.277 0.652 -0.452 0.444 Late Rut Rate -0.867 0.057 -0.859 0.062 -0.851 0.068 0.572 0.313 -0.179 0.773 -0.119 0.850 -0.330 0.588 Descriptive Ranking 8.3 8.0 8.0 4.5 3.0 2.3 3.3

MBV; D60; D30; and D10. Response variables were rut depths at 1,000 and 5,000 wheel passes, and the rut rate for both early and late traffic. The regression results are shown in Table 25; no combination was found significant at a 5-percent signifi- cance level. The regression equations indicate fine aggregate UVA, UVB, and VTM5 are good predictors of the rutting perform- ance of the fine-graded mixtures when tested in wet condi- tions. Only mixture FAM5 (Natural Sand B) showed an inflection point at approximately 5,000 APT wheel passes, which was presumably initiated by stripping. Based on the regression equations in Table 25, HMA rutting performance improves as the fine aggregate UVA increases. Sensitivity of fine aggregate UVA to APT traffic levels in the presence of moisture was analyzed at the 4.5-mm and 9-mm total rut depths. Again, 9 mm was chosen because it is the maximum amount of rutting experienced. Figures 35 and 36 show relationships between fine aggregate UVA and the num- ber of APT wheel passes in the presence of moisture required to reach 4.5- and 9-mm total rut depths, respectively. Both relationships appear to be exponential and significant. The exponential regression equation shown in Figure 35 indicates Figure 31. Rut depth/fine aggregate UVA relationship (moisture tests) (1,000 passes). Figure 32. Rut depth/fine aggregate UVA relationship (moisture tests) (5,000 passes). Figure 34. Late rut rate/fine aggre- gate UVA relationship (moisture tests). Figure 33. Early rut rate/fine aggre- gate UVA relationship (moisture tests). y = -1.87x + 96.25 R2 = 0.62 0 5 10 15 20 25 30 35 35 40 45 50 UVA, % R ut D ep th @ 1 ,0 00 W he el P as se s, m m y = -1.03x + 58.15 R2 = 0.76 0 5 10 15 20 25 30 35 35 40 45 50 UVA, % R ut D ep th @ 5 ,0 00 W he el P as se s, m m y = -0.39x + 21.09 R2 = 0.60 0 5 10 15 20 25 30 35 40 45 50 UVA, % Ea rly R ut R at e m m /L og N y = -1.98x + 98.65 R2 = 0.62 0 5 10 15 20 25 30 35 40 45 50 UVA, % La te R ut R at e, m m /L og N 31

32 the number of APT wheel passes required to reach a 4.5-mm total rut depth increases by approximately 16 for an increase in fine aggregate UVA from 45 to 50 percent. For this same 5- percent increase in fine aggregate UVA, the equation shown in Figure 36 indicates the number of APT wheel passes increases by approximately 55,500 to reach a 9-mm total rut depth. Again, at the lower rut depth, the change in fine aggre- gate UVA makes very little difference in the number of APT wheel passes; however, at the 9-mm rut depth, slight changes in the fine aggregate UVA appear to make a very large differ- ence in the number of wheel passes. Figure 37 presents the APT rut depths as a function of the number of wheel passes for each of the fine aggregates used in the moisture susceptibility portion of the study. The fine aggregates with lower UVA values of 40, 42 percent show much poorer performance than the fine aggregates that have UVA values of 46 through 49 percent. The three mixtures with high UVA have comparable performances in the APT in the presence of moisture. These results suggest a minimum fine aggregate UVA value of approximately 45 percent may Figure 35. Effect of fine aggregate UVA on traffic (moisture suscepti- bility) (4.5-mm rut depth). y = 0.28e0.10x R2 = 0.81 0 10 20 30 40 50 60 70 80 90 100 35 40 45 50 UVA, % W he el Pa ss es to 4 .5 m m R ut D ep th Table 25. Regression analyses for rutting parameters and fine aggregate UVA (moisture susceptibility tests). Response Variable Predictor Variable Model Equation R2 p-value UVA 94.86 – 1.84 UVA 0.74 0.063 UVB 102.95 – 1.86 UVB 0.73 0.064 Rut Depth @ 1,000 Wheel Passes1 VTM5 98.97 – 1.73 VTM5 0.72 0.0699 UVA 63.78 – 1.14 UVA 0.90 0.052 UVB 64.50 – 1.07 UVB 0.74 0.140 Rut Depth @ 5,000 Wheel Passes2 VTM5 61.54 – 0.99 VTM5 0.74 0.142 UVA 21.27 – 0.39 UVA 0.75 0.057 UVB 22.44 – 0.39 UVB 0.71 0.073 Early Rut Rate1 VTM5 21.61 – 0.36 VTM5 0.70 0.079 UVA 98.26 – 1.97 UVA 0.75 0.057 UVB 106.30 – 1.98 UVB 0.74 0.062Late RutRate1 VTM5 102.15 – 1.85 VTM5 0.72 0.068 1All mixtures included 2FAM1 (Natural Sand A) rut data is not available Figure 36. Effect of fine aggregate UVA on traffic (moisture suscepti- bility) (9-mm rut depth). y =2E-09e0.62x R2 = 0.79 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 35 40 45 50 UVA, % W he el P as se s t o 9 m m R ut D ep th Figure 37. Effect of fine aggregate UVA on rut depth (moisture susceptibility). 0 5 10 15 20 25 30 0 5000 10000 15000 20000 Number of Wheel Passes R ut D ep th , m m UVA=40, MBV=3.3, N9=25 UVA=46, MBV=1.3, N9=18,500 UVA=49, MBV=8.0, N9=10,000 UVA=49, MBV=5.1, N9=19,300 UVA=42, MBV=5.0, N9=950

help decrease rutting when the HMA mixture is exposed to moisture. The MBV for each mixture is also shown on the fig- ure. There does not appear to be any pattern to the rutting as a function of the MBV. Indeed the three mixtures showing the least amount of rutting have MBV ranging from 1.3 to 8.0. Fatigue Mixtures These tests were the research team’s attempt to produce fatigue cracking in the APT facility and the results were some- what mixed. Three of the HMA mixtures exhibited fatigue cracking; the other three mixtures never developed fatigue cracking due to the inability to control temperature in the APT facility. However, considering the limitations of the data, some general conclusions can be drawn from the fatigue testing por- tion of the experiment. The percent fatigue cracking (i.e., the percentage of cracked area to total area) as a function of the coarse aggregate UVA and FOE21, shown in Figures 38 and 39, respectively, indi- cates a trend in the data. As UVA or FOE21 increase, so do the number of APT wheel passes required to fatigue HMA mix- tures. Figures 38 and 39 also show best fit lines, equations of the lines, and R2 values. These are presented for informational purposes only. With three data points an excellent fit can nearly always be obtained. However, the trends appear valid and indicate that an HMA mixture containing coarse aggre- gates with UVA and/or FOE21 values in the 45- to 50-percent range would much better resist fatigue cracking than mix- tures with UVA and/or FOE21 values below 45 percent. Figure 40 is a plot of the percent fatigue cracking as a function of fine aggregate UVA for the three fine-graded mixtures. The data are obviously scattered because the FA-3 and FA-4 mixtures never exhibited fatigue cracking because of the inability to control ambient temperature. Both mixtures received 20,000 passes before testing was dis- continued due to rutting. Recent fatigue testing in the APT with temperature control suggests that these mixtures may have sustained additional passes (up to approximately 100,000) before failing in fatigue. From the data available, it is not possible to draw conclusions on the trend in the fatigue cracking as a function of the fine aggregate UVA relationship. Figure 39. Fatigue cracking/flat or elongated relationship. Figure 38. Fatigue cracking/coarse aggregate UVA relationship. Figure 40. Fatigue cracking/fine aggregate UVA relationship.y = -4.62x + 224.7 R2 = 0.99 0 10 20 30 40 50 40 42 44 46 48 50 UVA, % Pe rc en t C ra ck in g y = -1.46x + 69.2 R2 = 0.99 0 10 20 30 40 50 25 30 35 40 45 50 FOE21, % Pe rc en t C ra ck in g 0 10 20 30 40 50 40 42 44 46 48 50 UVA, % Pe rc en t C ra ck in g 33

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TRB's National Cooperative Highway Research Program (NCHRP) Report 557: Aggregate Tests for Hot-Mix Asphalt Mixtures Used in Pavements examines performance-based procedures to test aggregates for use in pavements utilizing hot-mix asphalt (HMA) mixtures and provides guidance on using these procedures for evaluating and selecting aggregates for use in specific mixture applications. The appendices to NCHRP Report 557 are available as NCHRP Web-Only Document 82: Validation of Performance-Related Test of Aggregates for Use in Hot-Mix Asphalt Pavements: Appendixes A through F.

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