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Chemomechanical Approaches to Predict Asphalt Pavement Behavior and
Performance
Troy Pauli, Western Research Institute Linbing Wang and Wenjuan Sun,
Virginia Polytechnic Institute
“ARC Deliverables/Products Presentation and Workshop”TRB 2015, Thursday, January 15, 2015, 9:00am - 5:00pm,
Marriott Marquis, Washington DC.
This research has been supported by
Federal Highway Administration U. S. Department of Transportation Under Cooperative AgreementNo. DTFH61-07-H-00009
Delft University of TechnologyFaculty of Civil Engineering and Geosciences Section of Road and Railway Engineering
& The Delft Healing Consortium
Acknowledgements
2
Asphalt Microstructure Model Team
• Asphalt composition (chemistry) drives binder rheological behavior, which in turn drives mechanical (performance) behavior.
• Crude source dictates asphalt composition (chemistry) but may be modified to produce desired rheological and mechanical (performance) behavior.
• Crude source variation hinders prediction of pavement behavior.• Fundamental theories of material properties coupled with multi-scale
modeling efforts implemented now insures that the technology needed to effectively modify and model asphalt pavement behavior will be available in the future.
Asphalt Microstructure Model Team
• Multiscale modeling endeavors provide a link between fundamental material properties and mechanical (performance) properties.
• A chemomechanical theory of asphalt binder behavior has developed which integrates asphalt composition (chemistry) to physicochemistry and binder rheology, which relates to pavement mechanical behavior.
• A deliverable of this research is source code of phase-behavior of asphalt binder (i.e., asphalt composition (chemistry) ) which is compatible with finite element methods to predict mechanical response.
• A chemomechanical modeling platform will provide chemical, physical and rheological input parameters for finite element models based on fundamental material properties and theories.
Asphalt Microstructure Model Team
Physicochemical Model of RAP Blending
2
Prediction of RAP Asphalt Flow
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
0.00 0.20 0.40 0.60 0.80 1.00RAP Mass Fraction, χRAP
Pred
icte
d ln
( η)
AAB-1-waxWMA/AAA-1-(RAP-1)
AAB-1-waxWMA/AAD-1-(RAP-2)
AAG-1-waxWMA/AAD-1-(RAP-2)
YNP-waxWMA/AAD-1-(RAP-2)
( ) ( )( ) ( ) ( )( )( ) 2.5
0 0 1blend RAP neat blend RAP a neat aRAP neat RAP neatx x K x x x xη η η
−= + − +
RAP-Asphalt Flow Properties
Asphaltene Mass Fraction (Iso-Octane)
0.0 0.1 0.2 0.3 0.4 0.5 0.6
G*
at 6
0C a
t 10
rad/
s., P
a
1e+0
1e+1
1e+2
1e+3
1e+4
1e+5
1e+6
1e+7
Y=75.147*exp(18.975X), R2=0.8596
Prediction of RAP Asphalt Flow
60.00
65.00
70.00
75.00
80.00
85.00
90.00
0.00 0.20 0.40 0.60 0.80 1.00
RAP Mass Fraction, χRAP
Pred
icte
d Ph
ase
Angl
e, δ
AAB-1-waxWMA/AAA-1-(RAP-1)
AAB-1-waxWMA/AAD-1-(RAP-2)
AAG-1-waxWMA/AAD-1-(RAP-2)
YNP-waxWMA/AAD-1-(RAP-2)
( )( )0
0
1
1SECI
asphaltene
χ
χ
≈ −
≈ −
δ δ
δ
RAP-Asphalt Flow Properties
Asphaltene Mass Fraction (Iso-Octane)
0.0 0.1 0.2 0.3 0.4 0.5 0.6
Phas
e An
gle
at 6
0C, 1
0 ra
d/s.
60
65
70
75
80
85
90
Y=-288.05X2+36.892X+87.192, R2=0.9067
Physicochemical Model of Wax Surface Structuring
2
Paraffin surface freezing “detected” byX-ray reflectivity and grazing incidence diffraction
X.Z. Wu, B.M. Ocko, E.B. Sirota, S.K. Sinha, M. Deutsch, B.H. Cao, M.W. Kim, Surface Tension Measurements of Surface Freezing in Liquid Normal Alkanes, Science, 261 (1993a) 1018-1020.
Wax Surface-Freezing in Complex Media
Artificial Bees: Paraffin in paraffin oil
Wax Surface-Freezing in Complex Media
Wax Surface-Freezing in Complex Media
1%
5%
10%
20%
Wax Surface-Freezing in Complex Media
Wax Surface-Freezing in Complex Media
y = 0.5595x - 0.0142R² = 0.8947
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 2 4 6
Cry
tallin
e M
ater
ial v
ia D
SC
Γ, fitting coefficient
y = 1.1936x + 0.6833R² = 0.9711
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0 2 4 6
Per
cent
Wax
Ext
ract
ed fr
om IE
C N
eutra
ls
Γ, fitting coefficient
Wax Surface-Freezing in Complex Media
Finite Element Model of Fatigue-Healing
Driven by Wax Structuring
2
Finite Element Modeling
2036
E∞
η
σσEm
E∞
Em η
σσ D
{ }σ εd d di
ji ij plastic d
TS X J
a
=
= +å&
& &G
Finite Element Modeling
1 2 3
2139
0
2
4
6
8
10
12
0 1 2 3 4
Dis
plac
emen
t/loa
ding
rate
slo
pe X
100
0
Roundness
“5”
Finite Element Modeling
2240
0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25 30
Hea
ling
Rat
e In
dice
s
Mass % Wax per IEC Neutral
dh/dt
dh2/dt
Healing and Presence of Wax
• The oils phase viscosity is driven by molecular weigh.• The maltene (i.e., the oils plus resins) may be considered a polar
associated solution, BUT, apparent molecular weight may be a big player in viscosity.
• The asphalt (maltene plus asphaltenes) is conveniently modeled as a nano-particle suspension. AGAIN, apparent molecular weight considerations may play a significant role in viscous flow.
• Activation energy models of viscosity work well to explain viscosity-temperature relationships of the nano-particle suspension model considered.
• The Oil’s molecular weight correlates with thermal contraction properties of asphalt, thus, thermal fatigue correlates with thermal contraction, ergo, oils molecular weight correlates with , thermal fatigue.
• Asphaltene content strongly contributes to stiffness of RAP mixtures.
Findings-Conclusions
Findings-Conclusions
• The “LIQUID” (behavior) of asphalt relates to fatigue-healing.• Surface crystallization (Surface Freezing) of paraffin wax in complex media
involves diffusion controlled crystallization. Bees are observed in both thermally and solvent treated materials.
• Crystals develop at a liquid-vapor interface due to lowering of the liquid-vapor surface free energy state relative to a liquid-solid + solid-vapor state.
• Oils and wax concentration are mutually related in terms of molecular weight, both of which relate to healing.
• Force-flux Coupling makes it difficult to pin-point a single compositional contributor to binder mechanical (rheological) behavior.
• IN SHROT: a chemomechanics model of bitumen behavior based in non-equilibrium statistical thermodynamics of asphalt microstructure is proposed to predict damage-healing phenomena.
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