Raveling is a common distress of asphalt pavements, defined as the removal of stones from the pavement surface. To predict and assess raveling quantitatively, a cumulative damage model based on an energy dissipation approach has been developed at the meso level. To construct the model, a new test method, the pendulum impact test, was employed to determine the fracture energy of the stone-mastic-stone meso-unit, while digital image analysis and dynamic shear rheometer test were used to acquire the strain rate of specimens and the rheology property of mastic, respectively. Analysis of the model reveals that when the material properties remain constant, the cumulative damage is directly correlated with loading time, loading amplitude, and loading frequency. Specifically, damage increases with superimposed linear and cosine variations over time. A higher stress amplitude results in a more rapidly increasing rate of damage, while a lower load frequency leads to more severe damage within the same loading time. Moreover, an example of the application of the model has been presented, showing that the model can be utilized to estimate failure life due to raveling. The model is able to offer a theoretical foundation for the design and maintenance of anti-raveling asphalt pavements.
. [J]. Frontiers of Structural and Civil Engineering, 2024, 18(6): 949-962.
Kailing DENG, Duanyi WANG, Cheng TANG, Jianwen SITU, Luobin CHEN. A cumulative damage model for predicting and assessing raveling in asphalt pavement using an energy dissipation approach. Front. Struct. Civ. Eng., 2024, 18(6): 949-962.
After short-term aging (rolling thin film oven, RTFO)
Retained penetration
%
71
ASTM D5
Retained ductility at 5 °C
cm
22
ASTM D113
Tab.1
Property
Unit
Test result
Density
g/cm3
2.702
Moisture content
%
0.3
75 μm-mesh passing rate
%
86.1
Tab.2
Fig.2
G∞/MPa
fc
k
me
WLF parameters
c1
c2
550
641.87
0.16
0.93
9.21
87.2989
Tab.3
Fig.3
Specimen type
Mastic
Stone
Test temperature (°C)
Number of
Replicates
Specimens
SG
SBS Asphalt + filler with F/A of 0.7
Granite
−10,0,10,20,30,40,50,60,70,80 (10 different temperatures)
3
60
SD
–
Diabase
–
3
60
SL
–
Limestone
–
3
60
SB
–
Basalt
–
3
60
Tab.4
Fig.4
Fig.5
Fig.6
Fig.7
Temperature (°C)
Significance
−10
0.656
0
0.060
10
0.126
20
0.074
30
0.191
40
0.991
50
0.261
60
0.570
70
0.222
80
0.594
Tab.5
Fig.8
Fig.9
Temperature (°C)
Significance
−10
0.730
0
0.389
10
0.052
20
0.153
30
0.651
40
0.246
50
0.752
60
0.899
70
0.407
80
0.092
Tab.6
i
Gi (MPa)
τi (s)
1
5.86 × 10−13
2.0 × 10−6
2
2.22 × 101
2.0 × 10−5
3
9.20 × 100
2.0 × 10−4
4
2.40 × 100
2.0 × 10−3
5
5.66 × 10−1
2.0 × 10−2
6
9.11 × 10−2
2.0 × 10−1
7
2.24 × 10−2
2.0 × 100
8
2.86 × 10−3
2.0 × 101
9
1.70 × 10−3
2.0 × 102
10
1.33 × 10−11
2.0 × 103
11
1.57 × 10−12
2.0 × 104
12
8.22 × 10−13
2.0 × 105
13
5.56 × 10−12
2.0 × 106
G∞
1.99 × 10−14
Tab.7
Fig.10
Fig.11
Temperature (°C)
Fitting parameters
R2
p-value of fitting parameters
a
b
a
b
−10
8.22 × 104
−1.35 × 10−2
0.90
0.003
0.000
0
2.53 × 105
−1.84 × 10−2
0.97
0.000
0.000
10
4.17 × 105
−1.98 × 10−2
0.94
0.002
0.000
20
1.10 × 106
−2.33 × 10−2
0.92
0.022
0.000
30
1.22 × 106
−2.08 × 10−2
0.93
0.005
0.000
40
2.95 × 105
−7.24 × 10−3
0.94
0.000
0.000
50
4.99 × 105
−1.15 × 10−2
0.93
0.000
0.000
60
2.33 × 105
−6.68 × 10−3
0.92
0.000
0.000
70
6.10 × 105
−1.36 × 10−2
0.89
0.002
0.000
80
3.03 × 105
−9.21 × 10−3
0.90
0.000
0.000
Tab.8
Fig.12
Fig.13
Fig.14
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