Modification of Dynamic Modulus Predictive Models for Asphalt ...

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Figure 17: Comparison of Values Using the Witczak Predictive Equation [32] Figure 18: Comparison of Values Using the Hirsch Model [32] The results indicate that the Witczak and Hirsch models have the similar accuracy predicting dynamic moduli of Argentina mixtures. Both of the models have good prediction at lower temperatures. At high temperatures, the predicted E* values tend to be larger than the measured values. 39
Effects of Fibers on Asphalt Concrete Mixture Dynamic Modulus This section summarizes the findings of two research projects: Serfass and Samanos’ study [33], and a Wuhan University of Technology study [34]. Four types of manufactured fibers were studied by Serfass and Samanos, and three types of manufactured fibers were studied by the Wuhan University of Technology. The conclusions of two studies are agreed showing that different fibers have notable strengthening effects on mixture dynamic modulus values. Serfass and Samanos Study [33] The comprehensive study performed various laboratory tests on mastics, mortars, and asphalt concretes. Two types of mixtures were tested: thin course mixes, and porous mixes. Static and dynamic moduli tests were performed only for the thin course mixes. Pavement conditions surveys were also conducted. Pavement skid resistance, sand patch depth, and cracking information were collected over more than ten years. The fibers studied included chrysotile, rock wool, glass wool, and cellulose. Some of the research conclusions were: Fibers could reduce the loss of binder in coating mastic, thus increasing a pavement’s resistance to moisture, aging, and fatigue damage; Attention needs to be paid on rutting resistance for thin wearing courses; and The static and dynamic modulus values of fiber-modified asphalt mixtures are distinctly higher than mixtures that use the same binders but without fibers. Wuhan University of Technology Study [34] The three types of fibers used in this study were cellulose, polyester and mineral fibers. Unconfined dynamic modulus tests were performed at five temperatures from -10°C to 54.4°C, and nine frequencies ranging from 0.1 Hz to 25 Hz. The results indicate adding fibers to asphalt mixtures can increase HMA dynamic modulus values. Lower phase angles at lower temperatures and higher phase angles at higher temperatures were observed for fiber-modified asphalt mixtures. Asphalt mixtures containing fibers tend to have lower loss modulus values at medium temperatures. 40
Page 1 and 2: Modification of Dynamic Modulus Pre
Page 3 and 4: CHAPTER 5: CONCLUSIONS AND RECOMMEN
Page 5 and 6: Table 36: Project Effect Calibratio
Page 7 and 8: Figure 38: G* Master Curve for Asph
Page 9 and 10: ACKNOLEDGEMENTS I would like to exp
Page 11 and 12: CHAPTER 1: INTRODUCTION BACKGROUND
Page 13 and 14: well-known procedures among them. T
Page 15 and 16: and Hirsch Models (Chapter 4), and
Page 17 and 18: Table 1. The percentages in Table 1
Page 19 and 20: The binder grading results showed b
Page 21 and 22: maximum stress, and maximum strain
Page 23 and 24: Gyratory Compactor (SGC) is used to
Page 25 and 26: Figure 3: Master Curve Construction
Page 27 and 28: The penetration viscosities were us
Page 29 and 30: V 1S V 2S Va ’ Vc Vv V m V 1P V 2
Page 31 and 32: Previous Evaluation of the Witczak
Page 33 and 34: Figure 6: Master Curve Comparison f
Page 35 and 36: Figure 9: Summary of Percent Error
Page 37 and 38: Figure 12: Measured Values vs. Pred
Page 39: Figure 15: Comparison of Values Usi
Page 43 and 44: Project Mix Number Table 5: Experim
Page 45 and 46: 500' 500' 500' 500' 500' 500' 500'
Page 47 and 48: Iowa DOT Demonstration Project The
Page 49 and 50: Percent Passing, % Table 7: Iowa De
Page 51 and 52: Percent Passing, % 100.0 90.0 80.0
Page 53 and 54: Percent Passing, % Table 9: Indiana
Page 55 and 56: Figure 28: Universal Testing Machin
Page 57 and 58: shear stress to achieve a preset st
Page 59 and 60: ORIGINAL MODEL EVALUATION A total n
Page 61 and 62: Witczak 2006 Model Predicted Dynami
Page 63 and 64: Hirsch Model Predicted Dynamic Modu
Page 65 and 66: Table 13: ANOVA Table for Hirsch Mo
Page 67 and 68: MN vs. MO Yes 0% vs. 4% Yes MN vs.
Page 69 and 70: Table 16: Volumetric Properties of
Page 71 and 72: | | where η = viscosity (Poise), |
Page 73 and 74: G*, Pa G*, Pa temperatures. As show
Page 75 and 76: Phase Angle 75 65 55 45 35 25 15 37
Page 77 and 78: (| | ) ( ( )) where | | = dynamic s
Page 79 and 80: calibration variable D IN, D IA , D
Page 81 and 82: Table 21: Regression Results of 0%-
Page 83 and 84: Modified Witczak Model Predicted Dy
Page 85 and 86: Modified Witczak Model Predicted Dy
Page 87 and 88: Table 22: Regression Results of RAS
Page 89 and 90: decreasing with increasing RAS cont
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CP1 IN , CP1 IA , CP1 MN , CP1 MO =
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Equation 34. where P 1 c = modified
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MODIFIED MODEL EVALUATION The afore
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Dynamic modulus E*, psi Dynamic mod
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Page 101 and 102:
Model Predicted E*, psi Model Predi
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Page 105 and 106:
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are larger than the variability of
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Dynamic modulus E*, psi 10000000 10
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5%, and 6% RAS contents. The δ cal
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0.1 0.05 3% RAS 5% RAS 6% RAS 0 -0.
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Table 29: Coefficient Correlation C
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1.5 1 0.5 0 -0.5 -1 -1.5 6% RAS -2
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Table 31: 2006 Witczak Modification
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10000 8000 6000 4000 3% RAS 4% RAS
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dummy variables. A statistical anal
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Table 32: Project Effect Calibratio
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Table 36: Project Effect Calibratio
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REFERENCES [1] New Hampshire Depart
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[26] D. W. Christen and D. A. Ander
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Table 39: Dynamic Modulus Test Resu
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Page 147 and 148:
Page 149 and 150:
APPENDIX B: DSR TEST RESULTS Table
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APPENDIX C: DSR FREQUENCY SWEEP TES
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Mix Temp. Freq. BC-21 BC-23 BC-24 B
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Mix Temp. Freq. BC-27 BC-28 BC-29 B
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29 1.585 2773000 52.32 4360000 47.8
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