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ICAO Seminar on Aerodrome Physical Characteristics and
Pavements By Dr.M.W.Witczak Invited Speaker Held at ICAO South American Regional Office Lima, Peru 6 – 9 August 2013
An Overview of the New AASHTO MEPDG Pavement
Design Guide
FEATURES OF THE AASHTO M-E PAVEMENT DESIGN GUIDE
Developed under the US NAS (National Academy of Sciences)–NCHRP (National Cooperative Highway Research program)
$10,000,000 – 7 Year Effort (Largest Single US Transportation Research Project in the History of the US)
Project Team Leaders AC/Flexible Pavements: Dr. M.W.Witczak Rigid Pavements: Dr.M.Darter
4
Introduction Road and Highways are a very significant cost for agencies to
construct, maintain and rehabilitate (US Infrastructure worth $1,000,000,000,000)
Pavement design is a very complex process that involves many variables as well as the variation of each variable. It is one of the most complex Civil Engineering structures to design because we demand a FS=1.0
Mechanistic concepts provide a more rational and realistic methodology for pavement design; however, pavement response models are mathematically very complex and do not have single closed form equation solution.
The M-E PDG provides a consistent and practical method to design a pavement for a desired level of reliability.
The MEPDG considers a wide range of AC Flexible pavement structural sections for : New pavement systems Overlay pavement systems
5
Conventional Flexible Pavements
Deep Strength HMA Pavements
Full-Depth HMA Pavements
"Semi-Rigid" Pavements
6
HMA Overlay over Existing HMA: New Existing AC Conventional AC AC Deep strength HMA pavements AC Full depth asphalt AC Semi-rigid pavements
HMA over JPCP
HMA over CRCP
7
HMA over Fractured JPCP Crack and Seat Rubbilization
HMA over Fractured CRCP Rubbilization
8
The primary distresses considered in the MEPDG for flexible pavements are: Permanent Deformation (rutting)
AC Layers Unbound Base/Subbase/Subgrade Layers Total Rut Depth
Fatigue Cracking Top Down-Longitudinal Cracking Bottom Up- Alligator Cracking
Thermal Cracking
In addition, pavement smoothness (IRI) is predicted based on these primary distresses and other factors.
9
Asphalt 10
Major Asphalt Pavement Distresses
Major pavement distresses Permanent deformation Fatigue cracking Transverse (Thermal) cracking
•How can we simulate these problems in the lab?
11
Hierarchical Input Process
Level 1 (High Reliability) Analysis of special problems Usually will incorporate Testing High Visibility/Risk/Cost Projects
Level 2 (Medium Reliability) Standard Design - Most Cases (Rigorous but practical)
Level 3 (Lower Reliability) Lower impact/risk projects
12
HIERARCHIAL APPROACH (AC MODULUS)
x
x,σII2
f(ψ)
f(ψ)
f(ψ)
x
x
x,σI2
LEVEL MIX BINDER RELIABILITY 1 E* Lab Test G*,δ Lab Test
2 E*Predictive equation
G*,δ Lab Test
3 E*Predictive equation
AC Grade to properties
x,σIII2
13
Hierarchical Approach in NCHRP 1-37A
Major Reasons for Presence in M-E PDG
Allows for a Quantifiable Decision to be Made, Based on Benefit / Costs Regarding the Utility of Using Detailed Engineering Tests and Data Collection / Analysis Techniques Relative to Simple, Empirical Correlations or Engineering Guesses
14
Hierarchical Approach in MEPDG
Major Reasons for Presence in M-E PDG
Provide Quantifiable Methodology for Agency to Prove Certain High Profile, High Importance and High Cost Projects Justified
“Most Advanced State of the Art Technology is
Mandated to Save Significant Cost Benefits”
15
Hierarchical Approach in MEPDG
Major Reasons for Presence in M-E PDG
Collary is also True
“Many Projects do not Require Sophisticated , Advanced Engineering Approaches”
Dynamic Modulus Test Protocol
Follow Latest AASHTO Protocols Test Factorial
5 Temperatures (14, 40, 70, 100, and 130 deg F) 6 Frequencies (25, 10, 5, 1, 0.5, 0.1 Hz)
Recommend 3 Replicates per Mix Recommend 3 LVDT’s per Specimen Critical Attention to Specimen Flatness/
Perpendicularity (Use Capping if Problem)
16
17
Dynamic Modulus Test
ARIZONA STATE UNIVERSITY 18
Compressive Dynamic Modulus (|E*|) and Phase Angle (φ)
Time, t
φ/ωσosinωt
εosin(ωt-φ)
σ, εσ0 ε0
0
0|*|εσ
=E itωφ =
Stress – Strain Relationship
)sin(0 tt ωσσ =
)sin(0 φωεε −= tt
0
0*εσ
=E
)sin()sin(*
0
0
φωεωσ−
=t
tE
Dynamic Modulus Test (Level 1)
20
Construction of E* Master Curve
AASHTO TP62-03
5 Temperatures: 14, 40, 70, 100 and 130 oF
6 Frequencies: 25, 10, 5, 1, 0.5 and 0.1 Hz
ARIZONA STATE UNIVERSITY 21
Manual Shifting
0.01
0.1
1
10
-8 -4 0 4 8Log Reduced Time, s
E* 1
06 psi
14 °F
40 °F
70 °F
100 °F
130 °F
SC-64-22
22
Construction of E* Master Curve
0.01
0.1
1
10
-10 -8 -6 -4 -2 0 2 4 6 8Log Reduced Time, s
Dyn
amic
Mod
ulu
s, 1
06 psi
14 oF
40 oF70 oF
100 oF
130 oF
14 oF
40 oF70 oF
100 oF
130 oF
Log Time, sTime, sTime, s
Dyn
amic
Mod
ulus
, 106
psi
Time-Temp. Superposition Use any arbitrary temperature
value as a reference Normally this value is set to be
at 70°F Shift E* test results at other
temp. to reference temp. by time-temp superposition
E* results are not changed Can calculate E* values at any
temp. and freq. from master curve
0.01
0.1
1
10
-10 -8 -6 -4 -2 0 2 4 6 8Log Reduced Time, s
Dyn
amic
Mod
ulus
, 106 p
si
ARIZONA STATE UNIVERSITY
0.01
0.1
1
10
-10 -8 -6 -4 -2 0 2 4 6 8Log Reduced Time, s
Dyn
amic
Mod
ulus
, 10
6 psi
14 oF
40 oF70 oF
100 oF
130 oF
14 oF
40 oF70 oF
100 oF
130 oF
Log Time, sTime, sTime, s
Dyn
amic
Mod
ulus
, 106
psi
0.01
0.1
1
10
-10 -8 -6 -4 -2 0 2 4 6 8Log Reduced Time, s
Dyna
mic M
odulu
s, 10
6 psi
8
6
4
2
0
-2
-4
-6
-8 0 20 40 60 80 100 120 140
Temperature, ºF
Log
Shi
ft F
acto
r, lo
g a(
T) Log a(T) = a T2 + b T + c
log( *) (log )Ee tr
= ++ +δ
αβ γ1
E* Master Curves Shifting Concept
ARIZONA STATE UNIVERSITY 24
Master Curve
0.01
0.1
1
10
-8 -4 0 4 8Log Reduced Time, s
E* 1
06 psi
14 °F40 °F70 °F100 °F130 °FPredicted
SC-64-22
Witczak Predictive Equation (WPE)
)log393532.0log313351.0603313.0(34
238384
4
220020010
10055.0)(000017.000396.00021.0872.3
82208.0058097.000284.0
)(00177.002923.024994.1E*log
η
ρρρρ
ρ
ρρ
−−−++−+−
+
+−−−
−+−=
f
abeff
beffa
e
VVV
V
Where E* = dynamic modulus (105 psi) η = binder viscosity (106 poise), log log η = Ai + VTSi logT T = pavement temperature (Kalvin), Ai = Intercept of Viscosity-Temperature Regression Equation VTSi = Slope of Viscosity-Temperature Regression Equation Va = Air voids (%) Vbeff = Effective Binder Content by Volume (%) ρ34, ρ38, ρ4 = Cumulative Retained on 3/4“, 3/8“, and #4 Sieves, respectively (%) ρ200 = Passing on #200 Sieve (%)
Dynamic Modulus Master Curve AC Surface with PG76-22
10
100
1,000
10,000
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0
E* (k
si)
Log Reduced Time (sec)
Dynamic Modulus Master Curves with Witczak Predictive Equation (PG76-22, AC Surface)
PG76, Class Y (AC Surface)
ARIZONA STATE UNIVERSITY 27
MC Sigmoidal Predictive Equation
tr = Time of loading at reference temperature δ = Minimum value of E* δ+α = Maximum value of E* β, γ = Parameters describing the shape of the sigmoidal function
logE* )(log1 rte γβαδ ++
+=
ARIZONA STATE UNIVERSITY 28
Time-Temperature Superposition: Shifting
tr = Time of loading at reference temperature t = Time of loading a(T) = Shift factor as a function of temperature T = Temperature
tr = )(Ta
t rt
)( tTa =
ARIZONA STATE UNIVERSITY
E* Master Curve Mathematical Formulation
log( *) (log )Ee tr
= ++ +δ
αβ γ1
[ ]log( ) log( ) log ( )t t a Tr = −And
Where:
E* = Dynamic Modulus (psi)
δ, α, β, and γ = Sigmoidal Parameters
tr = Reduced Time
t = Time (sec)
a(T) = shift factor dependent on temperature, T (in oF)
SOURCE OF VARIATION OF
METHODS
Final Master Curve Equation
[ ])log()log( 2
1*)log(
cbTaTteE
++−+++=
γβ
αδ
Optimized simultaneously to get the 7 parameters (δ, α , β, γ, a, b, and c)
Master Curve Equations
( )rlog tLog E*1 eβ+γ
α= δ +
+
tr = 1/fr
2log a(T) aTemp bTemp c= + +
( )( ) ( ) ( )rlog a T log t log t= −
( )effeq
17.6f2 a h
ν=
+
Definition of Time (Period)
T: Called “Period” but it is actually the time required for the response to begin repeating itself
The fundamentally accepted definition (exclusive of rheologists) is that:
T = tload = 1 / f
T
i.e., f = 10Hz implies 10cycles/sec
or tload = 0.1sec
(tload)
Typical Calculated Frequency Values as Function of Speed
Type Road Facility
Design Speed (mph)
Location Frequency (Hz)
Representative AC Layer (4”-12”)
Thin AC Layers Wearing
Surface (1”-3”)
Thick AC Layers
Binder/Base (3”-12”)
Interstate 60 Mid 15 - 40 45 - 95 12 - 25
Bottom 5 - 20 28 - 55 5 - 15
State Primary
45 Mid 10 - 30 35 - 70 15 - 20
Bottom 5 - 15 21 - 42 5 - 10
Urban Street
15 Mid 5 - 10 10 - 25 5 - 10
Bottom 1 - 4 7 - 14 1.5 - 5
Intersection 0.5 Mid 0.1 – 0.5 0.5 – 1.0 0.1 – 0.25
Bottom 0.05 – 0.25 0.25 – 0.5 0.05 – 0.15
Introduction to the Ai-VTSi Analysis
Relationships Used in the Ai-VTSi Analysis
Loglog ή(cp) = Ai + VTSi* Log Tr ή(cp) – in units of centipoise Tr – Rankine Temperature (Tr=Tf+459.7)
36
ASTM Ai-VTSi Viscosity Model log log η = A + VTS log TR
Viscosity in Witczak E* Model (Part of Level 2)
y = -2.5807x + 8.163R² = 0.9993
0.0
0.2
0.4
0.6
0.8
1.0
1.2
2.70 2.75 2.80 2.85 2.90 2.95 3.00 3.05
Log
Log
(Visc
) (c
P)
Log (Temp) (R)
Temperature - Viscosity Relationship
Relationships Used in the Ai-VTSi Analysis
Conversion of Pen (5 sec; 100 gm) to ή
ή (in Poise) = 10.5012-2.2601 * log Pen + 0.00389* (log Pen)^2
Relationships Used in the Ai-VTSi Analysis ή at Trb (Softening Point) = 13,000 Poise (Shell Oil) ή (cp) = ή (cs) * (1 / Gb)
1 Pa-s = 10 Poise
Summary of Ai-VTSi Values for Example (With Mix / Compaction Temperatures)
Impact of Aging Upon E* Master Curves
Change of E* Due to Field Aging Time for 2 Differing Environmental Locations
43
Advantages: E* allows hierarchical characterization takes care of aging takes care of vehicle speed can be linked to PG Binder E* approximates FWD back-calculated modulus provides rational mechanistic material property for
distress prediction FHWA – AASHTO test protocols available Distress predictive models available
Dynamic Modulus (E*)
44
Indirect Tension Creep Test
45
Beam Fatigue Test
46
Rotational Viscometer
spindle
torque
sample
samplechamber
47
Dynamic Shear Rheometer
height (h)
radius (r)
torque (T)deflection angle (Θ)
49
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1.0E+04 1.0E+05 1.0E+06 1.0E+07 1.0E+08
Total Number of ESALs
AC R
uttin
g (in
)
ESALs
Load Spectra
Actual Traffic load spectra yields higher levels of rutting and cracking compared to the classical E18KSAL’s.
Traffic repetitions is a significant parameter influencing pavement distress.
50
51
-
0.05
0.10
0.15
0.20
0.25
PG 82-22 PG 70-22 PG 64-22Binder Grade
AC R
uttin
g (in
)
Binder stiffness has a significant influence upon AC rutting.
As the binder stiffness increases, AC rutting decreases.
In fact, as the entire HMA mix stiffness increases, AC rutting decreases.
52
53
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30 40 50 60 70
Traffic Speed (mph)
AC
Rut
ting
(in)
Traffic Speed Influences The AC Rutting.
Creep Speed (Parking Lot, Intersection Analysis) Causes Much More Damage To The Pavement Compared To Faster Highway Speeds.
54
55
-
0.05
0.10
0.15
0.20
0.25
0.30
Phoenix Dallas Atlanta Minneapolis
Environmental Location
AC
Rut
ting
(in)
MAAT (47.2oF)
MAAT (62.1oF)
MAAT (66.5oF)
MAAT (75.1oF)
For all variables being the same, the higher the temperature of an environmental location, the higher the AC rutting becomes.
56
57
0
5
10
15
20
25
30
35
40
2 4 6 8 10 12 14
AC Thickness (in)
Alli
gato
r Cra
ckin
g (%
)
Criteria
Design
Use of M-E PDG Analysis as a Design Tool
AC thickness has a significant influence upon Alligator fatigue cracking. As the AC thickness increases, the amount of alligator (bottom-up) fatigue cracking decreases.
58
59
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00-50 -40 -30 -20 -10 0 10 20 30 40 50
Distance (in)
AC
Rut
ting
(in)
Wander = 0Wander = 10 inWander = 24 in
60
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00-50 -40 -30 -20 -10 0 10 20 30 40 50
Distance (in)
Bas
e R
uttin
g (in
)
Wander = 0Wander = 10 inWander = 24 in
61
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00-50 -40 -30 -20 -10 0 10 20 30 40 50
Distance (in)
Subg
rade
Rut
ting
(in)
Wander = 0Wander = 10 inWander = 24 in
The more channelized that the vehicular traffic becomes, the more severe the pavement rutting becomes.
The severity of the rutting is magnified for layers near the surface.
62
63
1 ft
3 ft 5 ft
10 ft
AC
Base
Natural SG Compacted SG
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
1 3 5 7 9 11
GWT Depth (ft)
Mod
ulus
of S
ubgr
ade
(psi
)
Compacted Subgrade
Natural Subgrade
Presence of GWT near / within unbound material layers can significantly alter the material moduli and hence increase pavement damage.
64
65
0
500
1000
1500
2000
2500
PG70-22 PG64-28 PG58-34Binder Grade
Ther
mal
Cra
ckin
g A
mou
nt
(ft/m
ile)
Binder stiffness has the greatest influence upon Thermal Fracture within a cold environment.
As the binder stiffness (or surface layer stiffness) increases, the AC Thermal Fracture increases.
66
67
0
500
1000
1500
2000
2500
Barrow Fargo Billings ChicagoEnvironmental Location
Ther
mal
Cra
ckin
g Am
ount
(ft/m
ile)
Min. Air Temp.(-47oF)
Min. Air Temp.(-34oF)
Min. Air Temp.(-29oF) Min. Air Temp.
(-15oF)
Thermal Cracking cumulatively increases over time. Combined property of binder content and air void has
an influence upon the Thermal Fracture. In general, AC Thermal Fracture decreases with an
increase of binder content and a decrease in air void.
68
ARIZONA STATE UNIVERSITY 69
Influence of AC Mix Stiffness on Alligator Cracking, (HAC = 1 in)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low E* Med E* Hi E*
AC Mix Stiffness
Allig
ator
Bot
tom
Up
% C
rack
ing
Refe
renc
e Ba
sed
Upon
600
0 ft
2 / 500
ft
SG Mr= 3 ksi SG Mr= 8 ksi SG Mr= 15 ksi SG Mr= 30 ksi
ARIZONA STATE UNIVERSITY 70
Influence of AC Mix Stiffness on Alligator Cracking, (HAC = 10 in)
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
Low E* Med E* Hi E*
AC Mix Stiffness
Allig
ator
Bot
tom
Up
% C
rack
ing
Refe
renc
e Ba
sed
Upon
600
0 ft
2 / 500
ft
SG Mr= 3 ksi SG Mr= 8 ksi SG Mr= 15 ksi SG Mr= 30 ksi
ARIZONA STATE UNIVERSITY 71
Influence of AC Thickness upon Alligator Cracking
0%
10%
20%
30%
40%
50%
60%
70%
0 2 4 6 8 10 12 14
AC Thickness (inch)
Alli
gato
r Bo
ttom
Up
% C
rack
ing
Ref
eren
ce B
ased
Upo
n 60
00 ft
2 / 500
ft
Med E*, Mr= 3 ksi Med E*, Mr= 8 ksi Med E*, Mr= 15 ksi Med E*, Mr= 30 ksi
ARIZONA STATE UNIVERSITY 72
Influence of Subgrade Modulus upon Alligator Cracking
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5000 10000 15000 20000 25000 30000 35000
Subgrade Modulus (psi)
Alli
gato
r B
otto
m U
p %
Cra
ckin
g R
efer
ence
Bas
ed U
pon
6000
ft2/
500
ft
ARIZONA STATE UNIVERSITY 73
Influence of AC Mix Air Voids upon Alligator Cracking
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12
AC Mix Air Voids (%)
Alli
gato
r B
otto
m U
p %
Cra
ckin
g R
efer
ence
Bas
ed U
pon
6000
ft2/
500
ft
ARIZONA STATE UNIVERSITY 74
Influence of Percent AC Binder by volume upon Alligator Cracking
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
6 7 8 9 10 11 12 13 14 15 16
AC Mix Effective Binder Content By Volume (%)
Alli
gato
r B
otto
m U
p %
Cra
ckin
g R
efer
ence
Bas
ed U
pon
6000
ft2/
500
ft
ARIZONA STATE UNIVERSITY 75
0
0.1
0.2
0.3
0.4
0.5
0.6
0 2 4 6 8 10 12 14 16
Thickness of Asphalt Layer (in)
Tens
ile S
trai
n
Eac = 100,000 Eac = 300,000 Eac = 500,000Eac = 1,000,000 Eac = 3,000,000
ARIZONA STATE UNIVERSITY 76
AC Rut Depth Prediction (M-E PDG)
Basic Model:
∑=2
1
pεz
zd dzR
3r2
1
β̂β̂r
r
p NTβ̂εε
=Lab Relationship
[ ] ( )[ ][ ]3r2r
1z
ββr
zo21yxz
zp NTβ β ZCC)σμ(σσ
E1ε ++−
=
( )3r2
1
β̂β̂rrp NTβ̂εε =
ARIZONA STATE UNIVERSITY 77
Unbound Base / Subbase / Subgrade Rut Depth Prediction (M-E PDG)
Basic Model:
∑=2
1
pεz
zd dzR
Not Linear Log-Log; Linear Semi log
β
Nρ
eεεβ̂
εε
r
o
r
p
−
=
=
−β
Nρ
eεεβ̂εε
r
orp
[ ]
+−
=
−β
Nρ
eεεβ̂)σμ(σσ
E1ε
r
oyxz
zp
ARIZONA STATE UNIVERSITY 78
Unbound Base / Subbase / Subgrade Rut Depth Prediction (M-E PDG)
(Cont’d)
β = f (wc) EICM / 1-37A Moduli – Moisture Interaction Critical here ∆w ∆Er
( )rr
o E β, ρ,fεε
=
Cο= f (Εr)
ρ = f (β)
[ ][ ] ( )[ ][ ]NEwf zcfc ,)σμ(σσE1ε yxz
zp β+−
=
Calibrated Model
Lytton Coefficients
ARIZONA STATE UNIVERSITY 79
Influence of MAAT upon Permanent Deformation
0.00
0.20
0.40
0.60
0.80
1.00
1.20
40 50 60 70 80
MAAT Temperature (oF)
AC
Rut
ting
(in)
Low AC E*
Medium AC E*
High AC E*
ARIZONA STATE UNIVERSITY 80
Effect of Traffic Speed upon Permanent Deformation
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 10 20 30 40 50 60 70Traffic Speed (mph)
AC
Rut
ting
(in)
AC Rutting Base Rutting Subgrade Rutting
ARIZONA STATE UNIVERSITY 81
Influence of AC Thickness upon AC Rutting as Function of Depth Within AC
Layer
0.00
0.02
0.04
0.06
0.08
0.10
0 1 2 3 4 5 6 7 8 9 10 11
Depth of Mid point of AC Sublayer (in)
AC
Rut
Dep
th a
t Sub
laye
r Mid
Poi
nt (i
n) 1 in AC Thickness
5 in AC Thickness
8 in AC Thickness
12 in AC Thickness
ARIZONA STATE UNIVERSITY 82
Effect of AC Thickness on Subgrade Rutting at Different Subgrade Modulus
(Medium AC Mix Stiffness)
0.000.050.100.150.200.250.300.350.400.450.50
0 2 4 6 8 10 12AC Thickness (in)
Subg
rade
Rut
ting
(in)
SG Modulus = 8000 psi
SG Modulus = 15000 psi
SG Modulus = 30000 psi
*3993.0*734.11552.31 10*1* NTk
r
p −= βεε
General εp/εr Relationship Used in the 2002 Design Guide
εp = plastic strain
εr = resilient strain
T = layer temperature (deg F)
N = no of load repetition
k1 = Confining Pressure, Depth Function.
βr1, βr2, βr3 = Calibration Factors
HMA Layer
βr1 βr2 βr3
Field Calibration Factors AC-Fatigue
3
24.1
5
11" f
f
1EKFβN
tff
ββ
σ ε−
=
Nf = number of repetitions to fatigue cracking
εt = tensile strain at the critical location
E = material stiffness
K1 = laboratory calibration parameter
βf1, βf2, βf3 = calibration factors
M-E PDG is the most powerful Pavement-Material Analysis-Design Tool ever developed.
M-E PDG will lead to a more fundamental analysis of the consequences associated with the material-structure - environmental interaction.
M-E PDG has the potential for increasing pavement performance and life while decreasing life cycle costs associated with new and rehab scenarios.
85
86
Implementation Considerations
Be careful of blind application of Modified asphalts in MEPDG.
E* value may be okay Distress performance prediction models (ac
rutting, fatigue cracking and thermal fracture) generally calibrated with conventional asphalt mixtures
Performance prediction of Modified AC Mixtures questionable
Suggest local calibration
87
Implementation Considerations
MEPDG is an excellent product and major enhancement to current technology; however the technology is still evolving: Do not expect perfect predictions
Need to locally calibrate to actual field performance Must be prepared to Conduct Trench Sections!!!!!!
Need to have a well defined nationally coordinated approach to develop planned model enhancements Reflective cracking Rutting and fatigue cracking model enhancements Chemically Stabilized Materials Calibration Performance of modified mixtures Refinement of level standard deviations for use in
reliability models