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Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies (2018)

Chapter: Chapter 4 - Analysis of Engineering Properties

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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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Suggested Citation:"Chapter 4 - Analysis of Engineering Properties." National Academies of Sciences, Engineering, and Medicine. 2018. Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25185.
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103 Analysis of Engineering Properties Statistical analyses were conducted to assess whether differ- ences exist between WMA and HMA for the binder properties, mix characteristics, in-place properties, and laboratory- measured engineering properties. For projects with one WMA and an HMA control, t-tests were used to compare the characteristics and properties that have replicate data with a 95% confidence level (α = 0.05). To assess statistical differ- ences within projects, the general linear model (α = 0.05) was conducted. Overall comparisons of such properties for WMA and HMA were also made using paired t-tests with the results from all projects. Binder Properties A paired t-test was performed to evaluate the overall effect of the WMA technology on several binder results [TCE extrac- tion binder content, true PG critical temperatures, DTc, LAS cycles at low- and high-strain levels, and JNR3.2 (non recoverable creep compliance at 3.2 MPa)]. The results shown in Table 4-1 indicate that no statistical difference was obtained at a signifi- cance level of α = 0.05 for the majority of the binder proper- ties. Only the binder content and the parameter JNR3.2 showed statistical differences. Results from the Multiple Stress Creep Recovery Test indicate that HMA recovered binders were more resistant to permanent deformation. Mixture Properties Volumetric Mix Properties During construction, mix samples were taken from the loaded trucks before they left the plant. For each sample, six specimens were compacted hot and six were compacted after reheating the mix to determine each mixture’s volumet- ric properties. All samples were compacted at the expected roadway compaction temperature of the respective mix. Samples were compacted without reheating on site in a Troxler Model 4141 Superpave Gyratory Compactor and in a Pine Model AFG1A Superpave Gyratory Compactor. Additional mix was brought to NCAT’s main laboratory and reheated before compaction on a Pine Model AFG1A Superpave Gyratory Compactor. Table 4-2 shows a summary of the volumetric properties for the hot-compacted samples, and Table 4-3 shows the results for reheated samples. Table 4-4 exhibits the results of the paired t-test for means of air voids, VMA, and VFA of samples compacted on site (hot) and reheated samples. No statistical differences were obtained at the 95% confidence level (α = 0.05). Mixture Engineering Properties Dynamic Modulus To assess statistical differences in dynamic modulus results, the general linear model (α = 0.05) was conducted on the test data measured at a frequency of 10 Hz, 1.0 Hz, and 0.1 Hz and temperatures of 4°C, 20°C, and 40°C. Thus, the general linear model was completed three times to assess statistical differ- ences at each temperature. Tukey’s Test (α = 0.05) was used to determine where these statistical differences occurred and how the mixtures grouped within each project. Results of Tukey’s Test for dynamic modulus results at three temperatures and three frequencies for mixtures from each location are summarized in Table 4-5 to Table 4-9. For each location, mixtures with the same letter were not statisti- cally different. It is important to note that the statistical group- ings are within each location; it should not be inferred that a mix from one location with an “A” grouping is similar to a mix from another location with an “A” grouping. Table 4-5 shows the results for the mixtures from the Wisconsin project. The Rediset WMA mixture had significantly lower stiffness than the HMA mixture at 4°C and the two lower frequencies. C H A P T E R 4

104 Variable HMA WMA Mean Difference P-value Significant Pb (TCE) Mean 5.22 5.44 -0.22 0.02 Y SD 0.27 0.21 Tc High Mean 84.9 83.9 1.0 0.28 N SD 6.1 5.1 Tc Intermediate Mean 26.6 26.6 0.0 0.42 N SD 3.9 3.0 Tc Low Mean -19.2 -19.5 0.3 0.42 N SD 5.9 3.9 Tc Mean -6.2 -5.2 1.0 0.14 N SD 3.6 2.3 LAS Low Strain Mean 696,642 513,434 183,208 0.15 N SD 640,729 376,790 LAS High Strain Mean 8,598 5,771 2,827 0.10 N SD 6,593 2,733 JNR3.2 Mean 0.118 0.077 0.04 0.05 Y SD 0.008 0.003 Table 4-1. Paired t-test for means on binder results. Location Mix ID Gmm Va (%) VMA (%) VFA (%) D/B Gse Pb (%) Pba (%) Pbe (%) Wisconsin Control 2.515 4.4 15.5 71.7 0.90 2.741 5.5 0.70 4.79 Rediset 2.527 4.4 15.0 70.8 0.89 2.749 5.3 0.81 4.54 Zycotherm 2.507 4.4 15.8 72.0 0.77 2.729 5.4 0.54 4.91 Alabama Low Va HMA 2.467 1.8 12.9 86.2 1.1 2.663 5.5 0.34 4.77 Low Va WMA 2.447 1.3 13.5 90.6 0.92 2.656 5.1 0.23 5.27 Adj. Va HMA 2.48 3.0 13.3 77.2 1.05 2.666 5.2 0.39 4.43 Adj. Va WMA 2.476 2.5 13.3 81.1 1.16 2.678 4.8 0.55 4.65 Tennessee HMA 2.596 6.2 16.1 61.3 1.26 2.826 5.0 0.95 4.15 WMA 2.570 5.3 16.7 68.1 1.03 2.829 5.7 0.99 4.8 North Carolina HMA MW–RAS 2.459 6.4 16.5 61.3 1.0 2.649 5.0 0.44 4.58 WMA MW–RAS 2.448 4.9 15.8 69.2 1.0 2.646 5.2 0.40 4.86 HMA PC–RAS 2.436 4.2 15.8 73.2 1.04 2.637 5.4 0.22 5.15 WMA PC–RAS 2.443 4.2 15.6 73 1.13 2.647 5.4 0.38 5.04 Indiana HMA 2.494 5.3 16.0 67.1 1.44 2.720 5.5 0.90 4.68 WMA 2.490 4.2 15.2 72.5 1.29 2.725 5.7 1.04 4.75 Table 4-2. Summary of hot-compacted volumetric mix properties.

105 Location Mix ID Gmm Va (%) VMA (%) VFA (%) D/B Gse Pb (%) Pba (%) Pbe (%) Wisconsin Control 2.515 4.2 15.4 72.4 0.90 2.741 5.5 0.70 4.79 Rediset 2.527 5.1 15.6 67.4 0.89 2.749 5.3 0.81 4.54 Zycotherm 2.507 4.2 15.6 73.1 0.77 2.729 5.4 0.54 4.91 Alabama Low Va HMA 2.467 1.9 13.1 85.5 1.09 2.666 5.5 0.39 4.8 Low Va WMA 2.447 1.2 13.4 91.1 0.92 2.656 5.1 0.23 5.27 Adj. Va HMA 2.480 3.1 13.3 76.8 1.05 2.666 5.2 0.39 4.43 Adj. Va WMA 2.476 2.9 13.6 78.9 1.16 2.678 4.8 0.55 4.65 Tennessee HMA 2.596 6.0 15.8 62.3 1.26 2.826 5.0 0.95 4.15 WMA 2.570 5.5 16.9 67.5 1.03 2.829 5.7 0.99 4.8 North Carolina HMA MW–RAS 2.459 6.7 16.8 60.1 1.0 2.649 5.0 0.44 4.58 WMA MW–RAS 2.448 5.1 16.0 68.1 1.0 2.646 5.2 0.4 4.86 HMA PC–RAS 2.436 4.0 15.6 74.5 1.04 2.637 5.4 0.22 5.15 WMA PC–RAS 2.443 4.1 15.5 73.5 1.13 2.647 5.4 0.38 5.04 Indiana HMA 2.494 5.1 15.8 68.0 1.44 2.720 5.5 0.96 4.68 WMA 2.490 4.3 15.2 71.7 1.29 2.725 5.7 1.04 4.75 Table 4-3. Summary of reheated volumetric mix properties. Variable Air Void, % Hot Air Void, % Reheated VMA Hot (%) VMA Reheated (%) VFA Hot (%) VFA Reheated (%) Mean 4.16 4.22 15.00 15.04 72.73 72.42 Variance 2.19 2.20 1.69 1.66 69.81 69.62 Pearson correlation 0.982 0.982 0.987 t Stat -0.782 -0.713 0.888 P-value 0.224 0.244 0.195 t-Critical 1.761 1.761 1.761 Table 4-4. Paired t-test for means of volumetrics. Frequency Mixture 4°C 20°C 40°C Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping 10 Hz Control 1,881.0 A 804.2 A 221.7 A Rediset 1,638.7 A 690.2 A 212.9 A Zycotherm 1,678.4 A 719.9 A 210.4 A 1 Hz Control 1,328.3 A 445.6 A 97.9 A Rediset 1,127.2 B 370.2 A 91.1 A Zycotherm 1,173.3 A B 399.6 A 91.2 A 0.1 Hz Control 864.8 A 230.3 A 46.8 A Rediset 706.7 B 182.5 A 43.1 A Zycotherm 758.7 A B 206.2 A 43.9 A Table 4-5. Summary of statistical analyses of E* Test results for Wisconsin.

106 Frequency Mixture 4°C 20°C 40°C Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping 10 Hz Low air void HMA 2,114.8 A 1,132.7 A 355.8 A Low air void WMA 2,001.3 A 968.1 B 248.9 B Adj. air void HMA 2,367.6 B 1,265.6 C 408.3 A Adj. air void WMA 2,181.9 A B 1,167.6 A C 376.9 A 1 Hz Low air void HMA 1,667.9 A B 731.6 A B 171.9 A Low air void WMA 1,515.8 A 577.2 A 105.6 B Adjusted air void HMA 1,862.5 C 814.3 C 200.8 A Adjusted air void WMA 1,725.9 B C 753.5 B C 180.7 A 0.1 Hz Low air void HMA 1,236.3 A B 430.1 A 79.6 A Low air void WMA 1,056.4 A 309.2 B 45.1 B Adjusted air void HMA 1,371.4 C 477.2 A 92.9 A Adjusted air void WMA 1,278.3 B C 437.2 A 82.1 A Table 4-6. Summary of statistical analyses of E* Test results for Alabama. Frequency Mixture 4°C 20°C 40°C Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping 10 Hz HMA 2,361.3 A 1,249.4 A 330.3 A WMA 2,178.5 A 974.3 B 208.5 B 1 Hz HMA 1,823.2 A 770.5 A 137.9 A WMA 1,610.1 A 538.8 B 78.2 B 0.1 Hz HMA 1,298.2 A 421.7 A 58.5 A WMA 1,070.7 A 257.3 B 35.1 B Table 4-7. Summary of statistical analyses of E* Test results for Tennessee.

107 Frequency Mixture 4°C 20°C 40°C Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping 10 Hz HMA 2,444.7 A 1,423 A 494.1 A WMA 2,415.6 A 1,423.9 A 478.5 A 1 Hz HMA 1,937.1 A 924.5 A 233.7 A WMA 1,896.5 A 933.6 A 228.6 A 0.1 Hz HMA 1,430.2 A 531.9 A 104.3 A WMA 1,388.5 A 546.1 A 104.8 A Table 4-9. Summary of statistical analyses of E* Test results for Indiana. Frequency Mixture 4°C 20°C 40°C Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping Avg. E* (ksi) Statistical Grouping 10 Hz HMA MW–RAS 2,072.7 A 1,013.3 A 280.8 A WMA MW–RAS 1,762.9 B 729 B 166.4 B HMA PC–RAS 1,941.1 A B 981.8 A 292.5 A WMA PC–RAS 1,789.2 A B 821.1 B 196.5 B 1 Hz HMA MW–RAS 1,558.3 A 611.1 A 128.2 A WMA MW–RAS 1,223.1 B 371.9 B 59.5 B HMA PC–RAS 1,466.4 A C 604.3 A 135.9 A WMA PC–RAS 1,290.1 B C 453.6 B 78.8 B 0.1 Hz HMA MW–RAS 1,088.5 A 333.7 A 59.1 A WMA MW–RAS 754.6 B 164.1 B 25.7 B HMA PC–RAS 1,036.1 A 336.8 A 61.8 A WMA PC–RAS 841.8 B 221.4 B 34.4 B Table 4-8. Summary of statistical analyses of E* Test results for North Carolina.

108 A dj . V a H M A A dj . V a W M A W M A H M A H M A W M A Lo w V a H M A H M A P C– RA S Co nt ro l Re di se t Z yc ot he rm H M A M W –R A S Av er ag e Ru t D ep th (m m ) Lo w V a W M A W M A P C– RA S W M A M W –R A S Figure 4-1. Hamburg Wheel-Tracking Test results and grouping. Table 4-6 shows the results for the mixtures from the Alabama project. No significant differences were detected between the HMA and WMA mixtures with the adjusted air voids. However, for the mixtures with low air void content, the HMA mixture was stiffer than the WMA mixture at 40°C and two of the three frequencies at 20°C. This difference may have been influenced by the substantially higher asphalt con- tent of the WMA mixtures from Alabama. The statistical comparisons for the Tennessee mixtures are summarized in Table 4-7. The HMA mixture was sig- nificantly stiffer than the WMA mixture at 20°C and 40°C at all three frequencies. These differences may have been influenced by the substantially higher asphalt content of the WMA mixture. Table 4-8 summarizes the statistical comparisons among the North Carolina mixtures. At almost all temperatures and frequencies, the HMA mixture was significantly stiffer than its corresponding WMA mixture. However, there was no sta- tistical difference between the mixtures based on type of RAS. Table 4-9 shows the results for the mixtures from the Indi- ana project. No significant differences were detected between these HMA and WMA mixtures. Overall, the use of a WMA technology appeared to result in lower dynamic modulus values for 30 out of the 72 com- parisons across all temperature–frequency combinations. However, 11 of those cases may have been affected by differ- ences in asphalt content. No statistical difference in dynamic modulus was identified between the mixtures using PC–RAS and MW–RAS. Hamburg Wheel-Tracking Test Figure 4-1 provides a summary of the Hamburg Wheel- Tracking Test results and statistical grouping. For each HMA–WMA pair, the WMA mixture rut depth results were greater. Results of a paired t-test for means of the rut depth (p-value = 0.010) indicated that WMA mixtures had statis- tically different (higher) rut depths than HMA mixtures at a 95% confidence level (α = 0.05). However, the difference between HMA and WMA rutting was not statistically signifi- cant for the Wisconsin and Indiana projects based on the Tukey’s Test pair comparison analysis. Although the differ- ences between HMA and WMA were statistically different for both sets of Alabama mixtures and the Tennessee mixtures, the higher asphalt content of the WMA mixtures likely influ- enced the results. For the North Carolina mixtures, there were no statistical differences between results for mixtures con- taining different types of RAS. It is important to note that none of the mixtures (HMA or WMA) had a level of rutting approaching failure in the Hamburg Wheel-Tracking Test. Flow Number Figure 4-2 shows a summary of the Flow Number Test results and statistical grouping. Flow number results of mixtures from the same project were compared using Tukey’s Test. Of the eight HMA–WMA pairs, five were dif- ferent based on Tukey’s Test with the HMA mixture having a higher flow number than its corresponding WMA mixture

109 Fl ow N um be r (c yc le s) A dj . V a H M A A dj . V a W M A W M A H M A H M A W M A Lo w V a H M A H M A P C– RA S Co nt ro l Re di se t Z yc ot he rm H M A M W –R A S Lo w V a W M A W M A P C– RA S W M A M W –R A S Figure 4-2. Flow number results and grouping. in each case. A paired t-test for means of the flow number also indicated that HMA mixtures had statistically higher flow number results than WMA mixtures (p-value = 0.001). However, higher asphalt content for some WMA mixtures likely influenced the results in three of the eight pairs. The type of RAS did not significantly affect flow number results. However, for each location the flow number results were well above the recommended criteria for the project’s design traffic. Bending Beam Fatigue Of the eight HMA–WMA pairs that were statistically ana- lyzed, only the Tennessee mixtures had statistically differ- ent fatigue test results at the low strain level based on Tukey’s Test, as shown in Table 4-10. The Tennessee WMA was one of the mixtures that had a significantly higher asphalt content compared to its corresponding HMA mixture. A paired t-test for means of the endurance limit for WMA and Location Mix ID Average Nf (high strain) Group Average Nf (low strain) Group Wisconsin Control 287,530 A 126,510,069 A Rediset 258,840 A 81,180,843 A Zycotherm 339,997 A 92,643,767 A Alabama Low Va H MA 53,803 A 4,074,857 A B Low Va W MA 107,263 A 7,841,577 A Adj. Va H MA 91,153 A 1,451,193 B Adj. Va WMA 76,497 A 4,453,407 A B Tennessee HMA 66,908 A 2,842,008 A WMA 59,745 A 1,105,585 B North Carolina HMA MW–RAS 45,565 A 1,830,803 A WMA MW–RAS 38,264 A B 861,843 A B HMA PC–RAS 17,427 A B 954,994 B WMA PC–RAS 25,221 B 820,699 B Indiana HMA 59,437 A 2,245,642 A WMA 89,964 A 2,473,384 A Table 4-10. Beam Fatigue Test results and grouping.

110 HMA mixtures indicated that no statistical difference was evident (p-value = 0.153). Likewise, a paired t-test for means of cycles to failure at 400 microstrain indicated no statistical difference (p-value = 0.471). Overlay Test Table 4-11 shows the Overlay Test results for all mixtures, along with the statistical groupings. For most projects, there was a distinct difference in average Overlay Test results between HMA and WMA mixtures, with WMA mixtures tending to have higher cycles to failure. A paired t-test to eval- uate the effect of WMA technologies indicated that statistical differences were evident (p-value = 0.006). On average, WMA mixtures had 119% higher cycles to failure than HMA mix- tures. However, of the 10 HMA–WMA comparable pairs, only three were different based on Tukey’s Test. These three pairs were the Alabama low air void HMA–WMA pair, the Tennes- see HMA–WMA pair, and the North Carolina MW–RAS HMA–WMA pair. For the first two pairs, the WMA mixture had a considerably higher asphalt content than its HMA com- panion. The Overlay tests conducted on the Wisconsin mix- tures at the lower temperature and displacement resulted in higher cycles to failure, but the statistical groupings were the same. Also, for the North Carolina project, it was surprising that the PC–RAS HMA mixture had higher cycles to failure than the MW–RAS HMA mixture. Flexibility Index The flexibility index results from all mixtures are given in Figure 4-3. Of the eight HMA–WMA pairs, six were statisti- cally different based on Tukey’s Test. As noted with the Over lay Test results, for three of the six statistically different HMA– WMA pairs, the WMA mixture had a considerably higher asphalt content than its HMA companion. A paired t-test was also performed to evaluate the overall effect of WMA technol- ogy. The results indicated that no statistical difference was evident between flexibility index results of HMA and WMA mixtures (p-value = 0.144). Statistical analyses were also conducted on the flexibility index results on cores obtained from the last field inspections. As shown in Figure 4-4, all cores from the two Texas projects had flexibility index results below 1.0, indicating extremely brittle mixtures. Both mixtures from Illinois had good flexi- bility index values. These were stone matrix asphalt mixtures. Location Mix ID Temperature (°C) Displacement (in.) Cycles Until Failure Statistical Group Avg. SD Wisconsin Control 10 0.015 792 752.1 A Rediset 1,320 – A Zycotherm 1,903 705.6 A Control 25 0.025 241 83.8 A Rediset 285 51.1 A Zycotherm 436 96.4 A Alabama Low Va H MA 25 0.025 19 0.6 A Low Va W MA 214 69.1 B Adj. Va HMA 24 8.4 A Adj. Va WMA 44 5.6 A Tennessee HMA 25 0.025 226 55.4 A WMA 807 148.2 B North Carolina HMA MW–RAS 25 0.025 125 78.6 A WMA MW–RAS 619 88.4 C HMA PC–RAS 215 54.9 A B WMA PC–RAS 333 142.2 B Indiana HMA 25 0.025 109 30.3 A WMA 158 71.1 A Note: – = only two samples available. Table 4-11. Overlay Test results and grouping.

111 FW–HMA FW–WMA H M A P G 6 4- 22 15 % R A P– 3% R A S H M A P G 5 8- 28 15 % R A P– 3% R A S Ev o 3G P G 6 4- 22 15 % R A P– 3% R A S H M A P G 6 4- 22 5% R A S Austin Gravel Quartz Illinois Fort Worth Fl ex ib ili ty In de x Figure 4-4. Flexibility index of cores from existing projects (FW = Fort Worth). A dj . V a H M A A dj . V a W M A W M A H M A H M A W M A Lo w V a H M A H M A P C– RA S Co nt ro l Re di se t Z yc ot he rm H M A M W –R A S Lo w V a W M A W M A P C– RA S W M A M W –R A S Figure 4-3. I-FIT results and grouping.

112 On average, the quartzite mix had a flexibility index of 10.1 and the gravel mix had a flexibility index of 7.9; however, there was no statistical evidence to conclude that these mixes had different flexibility index values at a confidence level of 95% (p-value = 0.27, two-sample t-test). Two groups were identified from Tukey’s Test. Results of tests on Austin and Forth Worth cores were not statistically different, but the Illi- nois results were statistically higher. Figure 4-5 shows the flexibility index results on cores from the new projects. The average flexibility index of the four sec- tions from the Alabama project were below 1.0; flexibility index results from the other new projects were better but still low. ANOVA was performed to determine if HMA and WMA were different for all projects with flexibility index results above 1.0. Only the Wisconsin project had a significant differ- ence among the sections (p-value of 0.017). Tukey’s Test indi- cated that the cores from the Rediset section had statistically higher flexibility index than the other two Wisconsin sec- tions. No significant differences were found with sections from Alabama, North Carolina, and Tennessee. Statistical Analyses of Other Tests Since energy ratio is an aggregated value (i.e., no replicate results), an analysis of variance was not possible. However, a paired t-test performed to evaluate the overall effect of WMA technology indicated no statistical difference between energy ratio results of HMA and WMA mixtures (p-value = 0.235). A paired t-test was also performed to evaluate the effect of WMA technology on the indirect tension creep compliance and strength results. This analysis also indicated that no sta- tistical difference was evident in the critical temperatures of HMA and WMA mixtures (p-value = 0.342). Similarly, a paired t-test performed to evaluate the effect of WMA technology on the Jc parameter indicated that no statistical difference was obtained between HMA and WMA mixtures (p-value = 0.111). Summary of Laboratory Performance Test Results Table 4-12 exhibits a summary of the analytical assess- ments of performance test results. A paired t-test was per- formed to evaluate the overall effect of the WMA technology. The results indicated that statistical differences were obtained at a significance level of α = 0.05 for WMA mixtures produc- ing higher overlay tester cycles until failure, greater Hamburg rut depths, and lower flow numbers. The results of the general linear model analysis and statis- tical grouping indicated that from the eight WMA to HMA comparable pairs that were statistically analyzed, there were dif- ferences between five pairs for the Hamburg Wheel-Tracking and Flow Number tests, one pair for the Bending Beam Fatigue Test, and six pairs for the Flexibility Index Test. In addition, three out of 10 pairs had statistically different cycles to failure in the Overlay Test. Available performance A dj . V a H M A A dj . V a W M A H M A W M A Lo w V a H M A H M A P C– RA S Co nt ro l Re di se t Z yc ot he rm H M A M W –R A S Lo w V a W M A W M A P C– RA S W M A M W –R A S Fl ex ib ili ty In de x Figure 4-5. Flexibility index of cores from new projects.

113 criteria were used to evaluate the overall performance of all mixtures. Table 4-12 shows that the majority of the mixtures (11 of 15) met the highest energy ratio criteria (high traffic level), and none of the mixtures met the minimum flexibility index cri- terion; however, seven out of 15 met the Semi-Circular Bend– LTRC minimum criterion for Level 1 traffic. In addition, only one mixture met the flow number high traffic-level criteria. Conversely, all mixtures passed the 0.5-in. Hamburg Wheel- Tracking Test criterion. Correlation Analysis Among Mixture Properties Pearson correlation analysis was performed to assess the strength of a linear association between results from two per- formance tests. Table 4-13 shows the R-value for the majority of the performance test results. Tests that had an R-value greater than 0.75 or less than −0.75 were considered strongly correlated; tests with R-values between 0.60 and 0.75 or −0.60 and −0.75 were considered moderately correlated. The fol- lowing correlations were obtained based on this analysis: • Moderate to strong inversely proportional correlation between flexibility index and dynamic modulus values, • Strong proportional correlation between flexibility index and Overlay Test cycles to failure, • Moderate to strong proportional correlation between flow number and dynamic modulus values, • Moderate inversely proportional correlation between Over- lay Test cycles to failure and dynamic modulus values, • Moderate proportional correlation between Overlay Test cycles to failure and Hamburg wheel-tracking rutting, and • IDT critical pavement temperature (°C), semi-circular bend–Jc value, and energy ratio values did not correlate with any other test results. Pearson correlation analysis was also performed to assess the strength of a linear association between performance test results and several mixture properties and mixture component properties. Table 4-14 shows the R-value com- binations of performance tests results and mixture proper- ties. The following correlations were obtained based on this analysis: • Moderate proportional correlation between ignition oven asphalt content and energy ratio, • Moderate proportional correlation between TCE extrac- tion asphalt content and Hamburg wheel-tracking rutting, Test Overall WMA Effect = 0.05 Within Project WMA Effect = 0.05 Comparison to Preliminary Criteria Hamburg Wheel-Tracking Significant p-value = 0.010 5 of 8 pairs All mixtures passed 0.5-in. Hamburg Wheel-Tracking Test criterion Flow Number Significant p-value = 0.001 5 of 8 pairs 2 of 15 had flow number < 3 ESALs (million) 4 of 15 met the 3 to <10 ESALs (million) 8 of 15 met the 10 to <30 ESALs (million) 1 of 15 met the > 30 ESALs (million) Bending Beam Fatigue Not significant p-value = 0.153 for endurance limit 1 of 8 pairs NA Energy Ratio NA 13 of 15 met energy ratio values > 1.0 Not significant p-value = 0.235 13 of 15 met energy ratio values > 1.30 11 of 15 met energy ratio values > 1.95 Thermal Cracking (IDT) Not significant p-value = 0.342 NA NA Overlay Significant p-value = 0.006 3 of 10 pairs NA Flexibility Index Not significant p-value = 0.144 6 of 8 pairs None met flexibility index Semi-Circular Bend–LTRC Not significant p-value = 0.111 NA 7 of 15 had Jc traffic 4 of 15 had Jc traffic Note: NA = not available. Table 4-12. Summary of analytical statistical results.

114 E* 10 Hz (4°C) E* 10 Hz (20°C) E* 10 Hz (40°C) BBF High Strain BBF Low Strain ER OT FI SCB -Jc HWTT FN E* 10 Hz (4°C) 1 E* 10 Hz (20°C) 0.97a 1 E* 10 Hz (40°C) 0.85a 0.95a 1 BBF High Strain -0.49 -0.48 -0.30 1 BBF Low Strain -0.56 -0.56 -0.40 0.94a 1 ER -0.24 -0.25 -0.28 0.26 0.34 1 OT -0.41 -0.56 -0.68b 0.09 0.11 0.35 1 FI -0.67b -0.73b -0.79a 0.09 0.19 0.51 0.79a 1 SCB-Jc 0.52 0.47 0.31 -0.34 -0.44 0.30 -0.01 -0.14 1 HWTT 0.02 -0.10 -0.28 -0.09 -0.16 0.22 0.69b 0.43 0.48 1 FN 0.62b 0.71b 0.80a -0.04 -0.08 -0.02 -0.38 -0.44 0.08 -0.09 1 IDT Temp. -0.43 -0.32 -0.27 -0.01 -0.02 0.12 -0.09 0.04 0.10 -0.08 -0.45 Note: BBF = bending beam fatigue, ER = energy ratio, OT = overlay test, FI = flexibility index, SCB = semi-circular bend, FN = flow number. aStrong correlation (> 0.75, < -0.75). bModerate correlation (0.60 to 0.75, -0.60 to -0.75). Table 4-13. Mixture performance test correlation. Variable E* 10 Hz (4°C) E* 10 Hz (20°C) E* 10 Hz (40°C) BB High Strain BB Low Strain ER OT FI Jc HWTT FN IDT Temp. Pb Ign. -0.33 -0.37 -0.38 0.45 0.55 0.69 a 0.54 0.57 -0.01 0.43 0.17 -0.17 Pb TCE -0.07 -0.07 -0.09 0.17 0.17 0.38 0.49 0.31 0.26 0.66 a 0.32 -0.02 Tc High 0.28 0.43 0.49 -0.48 -0.53 -0.43 -0.66 a -0.61a 0.08 -0.52 0.05 0.35 Tc Int. 0.70 a 0.74a 0.65a -0.62a -0.73a -0.26 -0.52 -0.56 0.51 -0.03 0.28 -0.05 Tc Low 0.52 0.46 0.34 -0.12 -0.28 0.03 -0.28 -0.46 0.61 a 0.08 -0.10 0.08 Gmm 0.25 0.12 0.00 0.32 0.27 0.54 0.31 0.11 0.27 0.33 0.15 -0.43 Gmb 0.22 0.15 0.11 0.36 0.25 0.33 -0.01 -0.17 0.49 0.29 -0.06 0.09 Vbe -0.68 a -0.71a -0.72a 0.27 0.31 0.18 0.37 0.38 0.03 0.25 -0.75a 0.57 D/B 0.78b 0.84b 0.81b -0.53 -0.54 -0.07 -0.33 -0.39 0.33 0.05 0.82b -0.39 Gsb -0.09 -0.24 -0.37 0.44 0.41 0.61 a 0.47 0.29 0.21 0.37 -0.26 -0.16 Gse 0.24 0.11 0.00 0.32 0.28 0.58 0.39 0.16 0.31 0.46 0.22 -0.43 Pba 0.61 a 0.61a 0.60a -0.05 -0.11 0.16 0.01 -0.16 0.25 0.29 0.86b -0.59 Pbe -0.74 a -0.75a -0.74a 0.17 0.24 0.08 0.34 0.40 -0.10 0.14 -0.75b 0.67a P200 0.65a 0.73a 0.68a -0.67a -0.64a -0.06 -0.27 -0.31 0.38 0.11 0.65a -0.17 Tc -0.65 a -0.61a -0.52 0.11 0.28 0.04 0.42 0.57 -0.59 0.02 -0.10 -0.09 LAS Low Strain -0.58 -0.51 -0.41 0.13 0.31 0.10 0.03 0.28 -0.32 -0.41 -0.33 0.47 LAS High Strain 0.24 0.31 0.41 -0.11 -0.04 0.40 -0.20 -0.20 -0.07 -0.01 0.61a -0.25 JNR3.2 0.27 0.22 0.17 0.09 0.14 0.16 0.02 0.05 -0.13 0.04 0.54 -0.60 a Note: Ign. = ignition. aModerate correlation (0.60 to 0.75, -0.60 to -0.75). bStrong correlation (>0.75, <-0.75). Table 4-14. Mixture performance test and mixture properties correlation.

115 • Moderate inversely proportional correlation between the true PG high critical temperature and Overlay Test and flexibility index, • Moderate proportional correlation between the true PG intermediate critical temperature and dynamic modulus, • Moderate inversely proportional correlation between the true PG intermediate critical temperature and bending beam fatigue, • Moderate inversely proportional correlation between the true PG low critical temperature and semi-circular bend–Jc, • Moderate inversely proportional correlation between vol- ume percentage of effective binder Vbe and dynamic mod- ulus and flow number, • Strong proportional correlation between the D/B ratio and dynamic modulus and flow number, • Moderate proportional correlation between Pba and dynamic modulus at intermediate and low temperatures, • Strong proportional correlation between Pba and flow number, • Moderate inversely proportional correlation between Pbe and dynamic modulus and IDT critical pavement temperature, • Strong inversely proportional correlation between Pbe and flow number, • Moderate proportional correlation between the percent of material passing the No. 200 sieve (P200) and dynamic modulus and flow number, • Moderate inversely proportional correlation between P200 and bending beam fatigue, • Moderate inversely proportional correlation between DTc and dynamic modulus at intermediate and low tempera- ture, and • Moderate inversely proportional correlation between JNR3.2 and IDT critical pavement temperature. DTc Analysis At the time that the mixtures in this study were being evaluated, the discussion of a DTc criterion among most researchers was based on binders tested after 20 h in the PAV. Therefore, most of the DTc data provided in this report are based on 20 h in the PAV. However, the revised mix design guidelines for mixtures containing RAS (AASHTO PP 78-17) include a minimum DTc criterion of −5°C for binders recov- ered from mixtures containing RAS after 40 h in the PAV. Table 4-14 shows correlation results on DTc for each mixture versus the results of the Cracking tests performed in the study. Using the strong and moderate correlation categories previously described, none of the Cracking Test results had a strong correlation to DTc. The resilient modulus from the Energy Ratio Test showed the strongest correlation with an R-value of −0.67. Table 4-15 presents the strong and moder- ate correlations using other properties. The strongest cor- relation was between DTc and mixture PG low. Identification of Common Effects Among Test Results A factorial analysis was performed in order to identify common factors among performance test results. Facto- rial analysis is an exploratory technique applied to a set of observed variables that seeks to find common underlying factors. The key concept of factorial analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent variable. Table 4-16 shows the results of the factorial analysis, using the acronyms of the tests they represent. This technique found that the majority of the observed variables have some common effects. Overall, 65.4% of the variance was associ- ated with eight variables grouped into two factors. Factor loadings can be interpreted like standardized regression coef- ficients. Factor loadings higher than 0.6 can be considered to provide moderate correlation. For instance, the variable OT has a correlation of 0.72 with Factor 1; therefore, it is a mod- erately strong variable. An important feature of factorial analysis is that the axes of the factors can be rotated within the multidimensional variable space. An orthogonal rotation method (Varimax Method) is typically used to maximize the factor loadings on all of the variables. In particular, it identifies a solution where, to the maximum extent possible, loadings/correlations in the rotated component matrix are between 1 and −1. From these two identified factors, common effects can be interpreted, allowing hypotheses on hidden effects, as follows: • Factor 1 has to do with stiffness of the mixture related to fatigue cracking susceptibility and thermal susceptibility. A positive sign of the loading indicates an increase of a value. In this case, as the overall dynamic modulus of the mixture decreases the resistance to fatigue and thermal cracking increase. • Factor 2 explains how an increase in the fracture resistance parameter Jc is related to an increase in Hamburg rut depth. In other words, mixtures that tend to be less susceptible to fatigue cracking tend also to be more susceptible to perma- nent deformation. Property R-Value Correlation Strength Percent RAP 0.62 Moderate Deleterious materials -0.81 Strong PG intermediate -0.75 Strong PG low -0.98 Strong Tc, RAS high -0.67 Moderate Tc, RAS low -0.64 Moderate Tc, mix high 0.64 Moderate Table 4-15. DTc correlations.

116 Effect of Material Properties on Laboratory Performance Hierarchical cluster analysis was performed to separate observations by similar groups (Table 4-17). Cluster separa- tion was performed taking into account properties that were not well explained by Pearson correlation analysis and con- sidering variables showing higher loading on factorials only. Cluster analysis is the collective name given to a number of algorithms for grouping similar objects into distinct catego- ries. It is a form of exploratory data analysis aimed at group- ing observations in a way that minimizes differences within groups while maximizing the differences between groups. Hierarchical clustering organizes observations in a tree structure, based on similarity or dissimilarity between clus- ters. The algorithm starts with each observation as its own cluster, and successively combines the clusters that are most similar. Two important properties of the algorithm are the distance measure and the linkage method. The distance mea- sure determines how similarity between clusters is measured. The most common distance measure (squared Euclidean dis- tance) was used in this study. The linkage method determines how clusters are combined and how the similarities between the merged cluster and the remaining clusters are computed. The distance between the centroids or the means of the clus- ters was the linkage method used in this study. With this analysis, Cluster 1 contains all variables with the highest linkage and lowest distance; conversely, Cluster 3 contains all variables with the lowest linkage and largest distance. Hierarchical clustering helped identify that—in explaining variability among clusters—WMA technology had significant effects on laboratory performance results. This statement is explained in the following paragraphs. Cluster 1 includes six mixtures, with five of the six using a WMA technology. On average, this cluster contains mix- tures with lower dynamic moduli, higher resistance to fatigue cracking (Overlay Test and Flexibility Index Test), lower resis- tance to permanent deformation (Hamburg Wheel-Tracking Test), and moderate susceptibility to thermal cracking (Indi- rect Tensile Test Tc). This observed cluster tends to have higher asphalt content, lower binder PG temperatures, higher VMAs, and lower D/B ratios. Cluster 2 includes five mixtures, with only two using WMA technology. On average, this cluster contains mixtures with intermediate dynamic moduli (all temperatures), lower to moderate resistance to fatigue cracking (Overlay Test and Flexibility Index Test), higher resistance to permanent defor- mation (lower Hamburg Wheel-Tracking Test), and higher susceptibility to thermal cracking (Indirect Tensile Test Tc). This observed cluster tends to have lower asphalt content, higher PG temperatures, lower VMA, and slightly higher D/B ratios than Cluster 1. Cluster 3 includes only HMA mixtures, except for the relatively stiff WMA mixture from Indiana. On average, this Factor Variables Showing Higher Loading on Factorialsa Loadingb Variance Among Variables Accumulated Variance Among Variables 1 E* 10 Hz (4°C)c E* 10 Hz (20°C)c E* 10 Hz (40°C)c ER OTc FIc Jc HWTT FN IDTc BBF -0.90c -0.97c -0.97c 0.38 0.72c 0.84c -0.32 0.28 0.23 -0.72c 0.39 45.2% 45.2% 2 E* 10 Hz (4°C) E* 10 Hz (20°C) E* 10 Hz (40°C) ER OT FI Jc c HWTTc FN IDT BBF 0.35 0.22 0.01 0.39 0.55 0.34 0.71c 0.85c -0.32 0.13 -0.33 20.2% 65.4% aLoading refers to correlation between a common effect—factorial, in this case—and an actual variable. bAnalysis considering V-rotation. Significant factors shown. cSignificant factors (>0.6, <-0.6). Table 4-16. Factorial analysis results.

117 cluster contains mixtures with higher dynamic moduli, lower resistance to fatigue cracking (Overlay Test and Flexibility Index Test), slightly higher Hamburg rutting, and lower sus- ceptibility to thermal cracking. The intermediate and low critical PG temperatures and D/B ratios differentiate Clus- ter 2 and Cluster 3. Dynamic Modulus Master Curve Parameters as Indicators of Cracking Susceptibility The parameter γ (gamma) obtained from the dynamic modulus master curve has been shown to relate to the width of the relaxation spectra and is related to the slope of the master curve (Bhattacharjee et al. 2012). Incorporation of aged binders or binders with hardening modifiers to a mixture leads to an increase in γ, which corresponds to a flattening of the master curve (lower susceptibility to frequency changes) and widen- ing of the relaxation spectra. For a standard logistic function, the inflection point modulus (−β/γ) is controlled by the aggregate structure and volumetric effects. At high tempera- tures, the aggregate structure begins to dominate the behav- ior of the mixture, while volumetric properties and binder stiffness control the behavior at lower temperatures. Incorpo- ration of aged binder or hardening modifiers to a mixture leads to a shift of the inflection point to lower frequencies. Figure 4-6 shows a plot of the relaxation spectra parameter versus the inflection point for all mixtures from the new proj- ects. As observed, hardening increases from WMA to HMA mixtures for all cases except the Alabama low air void and the Indiana mixtures. This type of analysis was recently used by Mensching et al. (2016) while defining failure criteria for low temperature performance of asphalt mixtures. Figure 4-7 shows the relationship between the inflection point and the flexibility index for all new project mixtures. A reasonable correlation was obtained between these parame- ters (R2 = 0.54) and the results follow the expected trend with regard to aging and cracking susceptibility. Figure 4-8 shows another reasonable correlation (R2 = 0.57) of the inflection point, this time with the dissipated creep strain energy at fail - ure from the Energy Ratio Test and Indirect Tensile Strength Test. Lower dissipated creep strain energy to failure is expected for mixtures containing aged binders, thus confirming the observed relationship between relaxation spectra shape parameters and cracking susceptibility of asphalt mixtures. As shown in Figure 4-9, the average dynamic modulus and phase angle data were plotted in black space for each of the mixtures. All plots show good uniformity in their respective black space diagrams, as noted by R2 values greater than 0.98 for fourth-order polynomial functions. Because of the interaction of the asphalt binder and aggregate, mixtures generally exhibit a peak phase angle at an intermediate dynamic modulus. This peak value is asso- ciated with the inflection point in the master curve (−β/γ). Variable Cluster 1 Cluster 2 Cluster 3 Mean SD Mean SD Mean SD E* 10 Hz (4°C) 1,821.5 194.6 2,062.4 94.3 2,397.3 39.8 E* 10 Hz (20°C) 789.8 103.6 1,052.7 91.3 1,340.5 96.0 E* 10 Hz (40°C) 202.7 19.6 311.0 53.5 427.8 74.9 OT 453.4 219.5 123.3 91.9 129.1 85.0 FI 4.82 1.61 2.00 1.29 1.56 1.35 Jc 0.43 0.10 0.52 0.16 0.54 0.07 HWTT 2.84 1.10 2.21 1.03 2.33 0.69 IDT Tc -19.2 0.753 -17.8 1.483 -20 0.816 Mix ID Wisconsin HMA Alabama Low Va WMA Alabama Adj. Va HMA Wisconsin WMA Alabama Low Va HMA Tennessee HMA Tennessee WMA Alabama Adj. Va WMA Indiana HMA North Carolina WMA MW–RAS North Carolina HMA MW–RAS Indiana WMA North Carolina WMA PC–RAS North Carolina HMA PC–RAS Pb (%) 5.64 5.17 5.29 Tc High 81.1 88.6 86.1 Tc Int. 24.3 27.9 29.8 Tc Low -23.4 -19.7 -17.1 VMA 15.8 14.4 14.5 D/B 0.94 1.04 1.36 Table 4-17. Cluster separation and properties.

118 MW–RAS–MW–RAS –B et a– G am m a MW–RAS NC HMA MW–RAS NC HMA PC–RAS Figure 4-6. Relaxation spectra parameter versus inflection point. Fl ex ib ili ty In de x I-F IT Beta–Gamma Parameter Figure 4-7. Inflection point versus flexibility index. Beta–Gamma Parameter D SC E H M A (k J/ m 3) Figure 4-8. Inflection point versus dissipated creep strain energy at failure.

119 R² = 0.9944 R² = 0.9882 R² = 0.992 0 5 10 15 20 25 30 35 40 4 4.5 5 5.5 6 6.5 Ph as e An gl e (d eg re es ) Log E* (psi) WI HMA WI Rediset WI Zycotherm Log E* (psi) Ph as e An gl e (d eg re es ) R² = 0.9955 R² = 0.9951 R² = 0.9958 R² = 0.9964 0 5 10 15 20 25 30 35 40 4 4.5 5 5.5 6 6.5 Low Va WMA Low Va HMA Adj. Va WMA Adj. Va HMA Log E* (psi) Ph as e An gl e (d eg re es ) R² = 0.9967 R² = 0.9946 0 5 10 15 20 25 30 35 40 4 4.5 5 5.5 6 6.5 TN HMA TN WMA Log E* (psi) Ph as e An gl e (d eg re es ) R² = 0.9934 R² = 0.9911 R² = 0.9948 R² = 0.9942 0 5 10 15 20 25 30 35 40 4 4.5 5 5.5 6 6.5 HMA MW–RAS WMA MW–RAS HMA PC–RAS WMA PC–RAS (c) Tennessee (a) Wisconsin (b) Alabama (d) North Carolina R² = 0.9971 R² = 0.9963 0 5 10 15 20 25 30 35 40 4 4.5 5 5.5 6 6.5 Ph as e A ng le (d eg re es ) Log E* (psi) IN HMA IN WMA (e) Indiana Figure 4-9. Black space diagrams for project mixes.

120 The greater the dynamic modulus inflection point (towards the right), the stiffer and/or more oxidized the mixture. The greater the phase angle inflection point, the more viscous the mixture. The phase angle represents the amount of energy a specimen can absorb, which indicates how well a specimen will resist cracking. A large peak phase angle indicates that a specimen will tend to deform before it cracks. Phase angle is one of the testing parameters for the Dynamic Modulus Test and is inversely related to the stiffness of the asphalt specimen. If the specimen has a relatively high stiffness at one frequency, it will tend to have a low phase angle at the same frequency. Potential cracking indicators could include the peak phase angle, the peak dynamic modulus value, or the −β/γ param- eter. For all cases—except for Indiana mixtures and Alabama adjusted air void mixtures—the dynamic modulus peak values of HMA mixtures were equal to or greater than the WMA mixtures, and the phase angle peak values were equal or lower for HMA mixtures (Figure 4-10). Figure 4-11 shows the −β/γ parameter for each mixture ranked from highest to lowest cracking susceptibility. The three mixtures with the highest values—and likely more resistant to intermediate temperature cracking—are WMA mixtures. On the other hand, most HMA mixtures tend to rank as more susceptible to cracking. Figure 4-12 shows the peak phase angle ranking for all mixtures. Higher phase angles are desirable to reduce cracking susceptibility. It can be seen that the six mixtures with the highest values were WMA mixtures. Table 4-18 shows a comparison of the Cracking Test param- eters ranking results for each test from best (1) to worst (15). Pe ak P ha se A ng le (d eg re es ) Dynamic Modulus Peak Value (ksi) NC MW–WMA NC PC–WMA NC PC–HMA NC MW–HMA Figure 4-10. Black space diagram peak values. A L A dj . V a H M A A L A dj . V a W M A IN H M A TN H M A IN W M A TN W M A A L Lo w V a H M A N C H M A P C– RA S N C W M A P C– RA S N C H M A M W –R A S N C W M A M W –R A S W I C on tr ol W I R ed is et W I Z yc ot he rm A L Lo w V a W M A –B et a– G am m a Pa ra m et er Figure 4-11. Ranking of mixtures by –a/f parameter.

121 From the overall combined rank, the top five are all WMA mixtures; and within each project, the WMA mixtures rank higher than the HMA mixtures. Bending beam fatigue results were not included in the analysis because of the difference in procedures used to obtain the number of cycles to failure on the first project. Since the Jc parameter showed poor correla- tion with the other Cracking Test parameters and, in several cases, did not follow the expected trend when comparing HMA and WMA mixtures, it was not included in this analysis. As expected, all WMA mixtures outperformed their respective HMA pairs, and overall, the top six cracking-resistant mix- tures were WMA mixtures. Low-Temperature Cracking Figure 4-13 shows the dynamic moduli at 4°C for each mixture ranked by stiffness at 10 Hz. The four mixtures with the lowest stiffness—and likely more desirable to resist AL Ad j. V a H MA AL Ad j. V a W MA IN HM A TN HM A IN W MA TN W MA AL Lo w Va HM A NC HM A P C– RA S NC W MA PC –R AS NC HM A M W– RA S NC W MA M W– RA S WI Co ntr ol WI Re dis et WI Zy co the rm AL Lo w Va W MA Pe ak P ha se A ng le (d eg re es ) Figure 4-12. Ranking of mixtures by peak phase angle. Mix ID DSCEf ER OT I-FIT Peak E* Peak Phase Angle Overall Combined Rank Wisconsin Control 7 6 6 7 13 11 4 8 Wisconsin Rediset 5 4 5 2 7 5 1 3 Wisconsin Zycotherm 6 8 3 9 11 12 3 5 Alabama Low Va HMA 13 13 15 14 8 9 15 14 Alabama Low Va WMA 10 12 9 8 3 3 8 6 Alabama Adj. Va HMA 15 14 14 15 10 8 13 15 Alabama Adj. Va WMA 12 11 13 13 6 6 14 12 Tennessee HMA 8 1 7 6 12 10 9 7 Tennessee WMA 3 7 1 3 4 2 5 2 North Carolina HMA MW–RAS 14 15 11 10 5 7 7 11 North Carolina WMA MW–RAS 1 10 2 1 1 1 2 1 North Carolina HMA PC–RAS 9 3 8 5 9 13 11 9 North Carolina WMA PC–RAS 4 9 4 4 2 4 6 4 Indiana HMA 11 2 12 12 15 15 10 13 Indiana WMA 2 5 10 11 14 14 12 10 Table 4-18. Comparison of rankings among cracking parameters.

122 low-temperature cracking—are WMA mixtures. The two Indiana mixtures are the stiffest at this temperature and frequency. An additional parameter to consider from the dynamic modulus data is the phase angle. As shown in Fig- ure 4-14, the rankings of phase angle at 4°C and 10 Hz show that WMA mixtures tend to absorb more energy than HMA mixtures, thus providing higher cracking resistance. Table 4-19 shows a comparison of the low-temperature dynamic modulus and phase angle parameters from other low-temperature tests. There are significant differences in the ranking provided by the IDT critical pavement temperature and the other parameters. The lowest IDT critical pavement temperature was −21°C for the Indiana HMA mixture. Several mixtures shared the same critical temperature, which indi- cates that this parameter lacks sensitivity. On the other hand, the low-PG temperature of the binder and DTc tend to agree in ranking and indicate that WMA mixtures may be more resistant to thermal cracking than HMA mixtures. A com- bined overall ranking using all five parameters also placed WMA mixtures above corresponding HMA mixtures. AL Ad j. V a H M A AL Ad j. V a W M A IN H M A TN H M A IN W M A TN W M A AL Lo w Va H M A NC H M A P C– RA S NC W M A P C– RA S NC H M A M W –R AS NC W M A M W –R AS W I C on tro l W I R ed ise t W I Z yc ot he rm AL Lo w Va W M A Lo w T em pe ra tu re E * (k si ) 3,000 2,500 1,500 2,000 1,000 500 0 Figure 4-13. Ranking of mixtures by dynamic modulus at 4çC and 10 Hz. AL Ad j. V a H M A AL Ad j. V a W M A IN H M A TN H M A IN W M A TN W M A AL Lo w Va H M A NC H M A P C– RA S NC W M A P C– RA S NC H M A M W –R AS NC W M A M W –R AS W I C on tro l W I R ed ise t W I Z yc ot he rm AL Lo w Va W M A Ph as e A ng le (d eg re es ) Figure 4-14. Ranking of mixtures by phase angle at 4çC and 10 Hz.

123 Table 4-19. Ranking comparison among low-temperature performance parameters. Mix ID Phase Angle at 4°C, 10 Hz E* Low at 4°C, 10 Hz IDT Critical Pavement Temp. True PG Low Temp. Tc Overall Combined Rank Wisconsin Control 4 5 4 3 5 4 Wisconsin Rediset 1 1 8.5 6 6 2 Wisconsin Zycotherm 3 2 12 9 7 6 Alabama Low Va HMA 11 9 15 11 11 10 Alabama Low Va WMA 8 7 12 12 12 9 Alabama Adj. Va HMA 13 13 4 14 14 15 Alabama Adj. Va WMA 12 11 12 13 13 13 Tennessee HMA 10 12 8.5 15 15 14 Tennessee WMA 7 10 4 10 8 8 North Carolina HMA MW–RAS 6 8 4 1 2 5 North Carolina WMA MW–RAS 2 3 8.5 2 1 1 North Carolina HMA PC–RAS 9 6 14 4.5 4 7 North Carolina WMA PC–RAS 5 4 8.5 4.5 3 3 Indiana HMA 15 15 1 8 10 12 Indiana WMA 14 14 4 7 9 11

Next: Chapter 5 - Mix Design Verifications »
Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies Get This Book
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TRB's National Cooperative Highway Research Program (NCHRP) Research Report 890: Using Recycled Asphalt Shingles with Warm Mix Asphalt Technologies documents the development of a design and evaluation procedure that provides acceptable performance of asphalt mixtures incorporating warm mix asphalt (WMA) technologies and recycled asphalt shingles (RAS)—with and without recycled asphalt pavement (RAP)—for project-specific service conditions.

Since the introduction of the first WMA technologies in the U.S. about a decade ago, it has quickly become widely used due to reduced emissions and production costs of mixing asphalt at a lower temperature. The use of RAS has increased significantly over the past 10 years primarily due to spikes in virgin asphalt prices between 2008 and 2015. The report addresses the amount of mixing between RAS binders and virgin binders when WMA is used.

It provides additional guidance for designing, producing, and constructing asphalt mixtures that use both RAS and WMA to address several gaps in the state-of-the-knowledge on how these two technologies work, or perhaps, don’t work together.

The report also identifies ways to minimize the risk of premature failure due to designing and producing mixes containing WMA technologies and RAS with poor constructability and durability.

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