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approximately a 15-mm rut depth at 20,000 wheel passes. 100
Natural Sand A is the poorest fine aggregate-yielding in
excess of 30 mm of rutting after approximately 1,000 APT
90
wheel passes.
Tensile Strength Ratio, %
80
Moisture Susceptibility Mixtures
In NCHRP Report 405, Kandhal and Parker recommended
70
the methylene blue value of the p0.075 fraction of the fine y = -1.60x + 94.46
aggregates as the best predictor for the stripping potential of R2 = 0.23
fine-graded HMA pavements. They used AASHTO T 283 and 60
the GLWT tracking device to investigate the influence of fines
in fine aggregate on stripping. The GLWT test was conducted
50
under water maintained at 50°C and an inflection point on a 0 2 4 6 8 10
plot of rutting versus number of passes was used as the param- MBV
eter for evaluating mixture stripping performance. In the cur-
Figure 30. TSR/MBV relationship.
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 rutting rates for both early and late traffic. The results are
an obvious inflection point. Mixture FAM3 (Granite Sand) shown in Table 24. Rutting data for mixture FAM1 (Natural
exhibited a slight change in slope that is possibly an indication Sand A) was not available for 5,000 wheel passes. It can be seen
of stripping. 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
Methylene Blue Test
are not significant. The MBV values have poor/fair positive
The tensile strength ratio (TSR) determined in the correlations with the rutting parameters. Particle size param-
AASHTO T 283 test was used to study the effect of the fine eters have poor correlations with the rutting parameters. The
aggregate on the moisture susceptibility of the HMA mix- relationships between each of these rutting parameters and
tures. Kandhal and Parker (1) suggested that the moisture fine aggregate UVA are shown in Figures 31 through 34.
susceptibility of fine-graded HMA mixtures was related to the A descriptive ranking was assigned to each test method as
MBV of fine aggregates. The goal of the current study was to shown in Table 24. The table shows that fine aggregate UVA
validate the effect of fine aggregate properties on HMA mois- has the highest ranking of 8.3 followed closely by fine aggre-
ture susceptibility. gate UVB and VTM5 tests at 8.0. The MBV has a ranking of
The correlation between TSR and the MBV of the fine 4.5 and the remaining tests have rankings of 3 or less. The
aggregates was investigated, but no significant correlation was rankings would seem to confirm that the fine aggregate UVA
found. A plot of TSR as a function of MBV is shown in Figure is the best test for predicting HMA mixture performance.
30. The TSR value of 100 percent for the FAM1 (Natural Sand Regression analyses were performed to determine if com-
A) mixture seems out of place given the MBV of 3.3. Further binations of aggregate test variables would provide an
investigation revealed that the FAM1 mixture also had the least improved relationship with rutting parameters. The inde-
p0.075 material (3.0 percent). This amount may be low pendent variables were fine aggregate UVA, UVB, and VTM5;
enough that the poor quality of fines did not affect its tensile
strength; however, the figure suggests some trend in the data. Table 24. Correlation matrix for rutting parameters and
Given the variability of the AASHTO T 283 test results, a clear fine aggregate properties (moisture susceptibility tests).
relationship between HMA moisture susceptibility as meas-
ured by the test and the MBV test is not readily apparent. UVA UVB VTM5 MBV D60 D30 D10
Rut depth at -0.858 -0.856 -0.847 0.555 -0.293 -0.235 -0.462
1,000 Passes 0.063 0.064 0.070 0.332 0.632 0.704 0.434
Rut depth at -0.948 -0.860 -0.858 0.257 -0.601 -0.453 -0.281
Rutting 5,000 Passes 0.052 0.140 0.142 0.743 0.399 0.547 0.719
Early Rut -0.867 -0.842 -0.834 0.666 -0.308 -0.277 -0.452
Given that only one mixture exhibited an obvious inflection Rate 0.057 0.073 0.079 0.220 0.614 0.652 0.444
point, rutting parameters were also used to evaluate the effect Late Rut -0.867 -0.859 -0.851 0.572 -0.179 -0.119 -0.330
Rate 0.057 0.062 0.068 0.313 0.773 0.850 0.588
of moisture on HMA performance. Parameters evaluated Descriptive 8.3 8.0 8.0 4.5 3.0 2.3 3.3
included rut depths at 1,000 and 5,000 APT wheel passes and Ranking
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35 30
y = -1.87x + 96.25 y = -0.39x + 21.09
30 R2 = 0.60
R2 = 0.62 25
25
Rut Depth @ 1,000
Wheel Passes, mm
20
Early Rut Rate
mm / LogN
20
15
15
10
10
5 5
0 0
35 40 45 50 35 40 45 50
UVA, % UVA, %
Figure 31. Rut depth/fine aggregate Figure 33. Early rut rate/fine aggre-
UVA relationship (moisture tests) gate UVA relationship (moisture
(1,000 passes). tests).
35 30
y = -1.03x + 58.15 y = -1.98x + 98.65
30
R2 = 0.76 25 R2 = 0.62
25
Rut Depth @ 5,000
Wheel Passes, mm
20
Late Rut Rate,
20
mm / LogN
15
15
10
10
5 5
0
0
35 40 45 50
35 40 45 50
UVA, %
UVA, %
Figure 32. Rut depth/fine aggregate Figure 34. Late rut rate/fine aggre-
UVA relationship (moisture tests) gate UVA relationship (moisture
(5,000 passes). tests).
MBV; D60; D30; and D10. Response variables were rut depths regression equations in Table 25, HMA rutting performance
at 1,000 and 5,000 wheel passes, and the rut rate for both early improves as the fine aggregate UVA increases.
and late traffic. The regression results are shown in Table 25; Sensitivity of fine aggregate UVA to APT traffic levels in the
no combination was found significant at a 5-percent signifi- presence of moisture was analyzed at the 4.5-mm and 9-mm
cance level. total rut depths. Again, 9 mm was chosen because it is the
The regression equations indicate fine aggregate UVA, maximum amount of rutting experienced. Figures 35 and 36
UVB, and VTM5 are good predictors of the rutting perform- show relationships between fine aggregate UVA and the num-
ance of the fine-graded mixtures when tested in wet condi- ber of APT wheel passes in the presence of moisture required
tions. Only mixture FAM5 (Natural Sand B) showed an to reach 4.5- and 9-mm total rut depths, respectively. Both
inflection point at approximately 5,000 APT wheel passes, relationships appear to be exponential and significant. The
which was presumably initiated by stripping. Based on the exponential regression equation shown in Figure 35 indicates
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Table 25. Regression analyses for rutting parameters 20000
and fine aggregate UVA (moisture susceptibility 18000
y =2E-09e0.62x
tests). R2 = 0.79
16000
Wheel Passes to 9 mm Rut Depth
Response Predictor
Variable Variable Model Equation R2 p-value 14000
Rut UVA 94.86 1.84 UVA 0.74 0.063
Depth @ 12000
1,000 UVB 102.95 1.86 UVB 0.73 0.064
Wheel 10000
Passes1 VTM5 98.97 1.73 VTM5 0.72 0.0699
Rut 8000
UVA 63.78 1.14 UVA 0.90 0.052
Depth @
5,000 UVB 64.50 1.07 UVB 0.74 0.140 6000
Wheel
Passes2 VTM5 61.54 0.99 VTM5 0.74 0.142 4000
UVA 21.27 0.39 UVA 0.75 0.057 2000
Early
Rut UVB 22.44 0.39 UVB 0.71 0.073
Rate1 0
VTM5 21.61 0.36 VTM5 0.70 0.079 35 40 45 50
UVA 98.26 1.97 UVA 0.75 0.057 UVA, %
Late Rut
UVB 106.30 1.98 UVB 0.74 0.062 Figure 36. Effect of fine aggregate
Rate1
VTM5 102.15 1.85 VTM5 0.72 0.068 UVA on traffic (moisture suscepti-
1
All mixtures included bility) (9-mm rut depth).
2
FAM1 (Natural Sand A) rut data is not available
Figure 37 presents the APT rut depths as a function of the
100
number of wheel passes for each of the fine aggregates used
90 y = 0.28e0.10x in the moisture susceptibility portion of the study. The fine
R2 = 0.81 aggregates with lower UVA values of 40, 42 percent show
Wheel Passes to 4.5 mm Rut Depth
80
70
much poorer performance than the fine aggregates that have
UVA values of 46 through 49 percent. The three mixtures
60
with high UVA have comparable performances in the APT in
50 the presence of moisture. These results suggest a minimum
40 fine aggregate UVA value of approximately 45 percent may
30
20 30
10 UVA=40, MBV=3.3, N9=25
0 25
UVA=46, MBV=1.3, N9=18,500
35 40 45 50
UVA, % UVA=49, MBV=8.0, N9=10,000
20
Figure 35. Effect of fine aggregate
Rut Depth, mm
UVA=49, MBV=5.1, N9=19,300
UVA on traffic (moisture suscepti-
bility) (4.5-mm rut depth). UVA=42, MBV=5.0, N9=950
15
the number of APT wheel passes required to reach a 4.5-mm
total rut depth increases by approximately 16 for an increase 10
in fine aggregate UVA from 45 to 50 percent. For this same 5-
percent increase in fine aggregate UVA, the equation shown 5
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- 0
gate UVA makes very little difference in the number of APT 0 5000 10000 15000 20000
Number of Wheel Passes
wheel passes; however, at the 9-mm rut depth, slight changes
in the fine aggregate UVA appear to make a very large differ- Figure 37. Effect of fine aggregate UVA on rut depth
ence in the number of wheel passes. (moisture susceptibility).
