Development and Evaluation of

Performance Tests to Enhance Superpave Mix

Design and its Implementation in Idaho

Fouad Bayomy SJ Jung

Richard NielsenThomas Weaver

DTOS59-06-G-00029 (NIATT Project No. KLK479)ITD Project No. RP 481 (NIATT Project No. KLK483)

1

Team Fouad Bayomy (PI),

Dynamic properties, and overall project phases

SJ Jung, Fracture and Fatigue studies

Richard Nielsen, Reliability Studies

Thomas Weaver, Constitutive Modeling

Graduate Students Ahmad Abu Abdo Baek, Seung Il

ITD Coordinator (s) Mike Santi (Main Contact) Ned Parrish (Research Mgr) Others?? To be identified…..

USDOT Ashley Bittner / Ed Weiner,

COTR Paul Ziman (FHWA, Boise

Office) Others…

External Testing / Consulting Idaho Asphalt Supply, Inc. Image analysis (Masad at TTI) X-Ray Tomography (WSU or

UT, Austin) NIATT and CE Support

Judy LaLonde Don Parks Others

2

Why this project?

ITD Moves towards SuperpaveSuperpave Mix Design System

Implementation of M-E DesignM-E Design guide at the national level

3

Project ObjectivesProject Objectives

Evaluate E* for Mixes that are commonly used in Idaho.

Determine E* vs. Temp for various binders used in Idaho, especially for polymer modified asphalts.

Develop constitutive models and develop procedure to estimate E* from given mix design properties.

Study of Gyratory Stability (GS) in relation to E*. Develop and evaluate mix fracture indicators for

ITD mixes. Incorporate reliability analysis.

4

Funding US DOT $ 280 k ITD $ 150 k UI Match (in kind) $ 154k

Total $ 584 k

5

Scope of Work

Phase A (Deformation study) Phase B (Fatigue and Fracture Study) Phase C (Implementation and Training) Phase D (Reporting)

6

Phase A: Mix Resistance to Deformation (E* and GS/CEI)

Tasks Literature Review Analytical Analysis Agg and Binders Evaluation Preparation and Evaluation of HMA

Mixtures Data Analysis Phase A Reports

7

Phase B: Mix Resistance to Fracture and fatigue Cracking

Tasks Literature Review Analytical Analysis Fracture Test Development Preparation and Evaluation of HMA

Mixtures Data Analysis Phase B Reports

8

Phases C and D: Implementation, Training & Reports

Tasks Work with ITD to Implement the Products Develop a training workshop to

disseminate the products Final Report / Peer Review

9

Project Progressas of Dec. 31, 2007

10

Progress

11

Phase / TaskQuarter

Month 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11

Task A1 – Review of previous studies and available data

10% 10% 2% 0% 2% 6% 30%

Task A2 – Analytical Analysis 12% 2% 4% 0% 18%

Task A3 – Experimental Design, Binder and Agg. Eval.

15% 15% 10% 5% 15% 60%

Task A4 – Prep and Evaluation of Asphalt Mixtures

5% 5% 10% 15% 35%

Task A5 – Data Analysis 10% 10%

Task B1 – Literature Review 10% 15% 5% 5% 5% 5% 45%

Task B2 – Finite Element Analysis 5% 5% 5% 15%

Task B3 – Development of the Fracture Test Procedure

12% 2% 14%

Task B4 – Prep and Evaluation of Asphalt Mixtures

0%

Task B5 – Data Analysis 0%

Task B6 – Reliability Analysis 0%

Task C1 – Development of Implementation Plan 0%

Task C2 – Training Program for ITD Personnel 0%

Tasks A6, B7 and C3 – Quarter Reports for USDOT

R1 R2 R3 R4 R5 R6 R7 Final 0%

Task D1: External peer review of the final report 0%

Task D2: Final report review by ITD 0%

Task D3: Final Report Submittal 0%

Phase C: Implementation of Research Products and Training

Reporting

Phase D: Final Report Review and Submittal

Q2 Q3 Q4Calendar Yr 2007

Year 1 Year 2 Year 3

Phase A: Evaluation of Mix Resistance to Deformation

Phase B: Evaluation of Mix Resistance to Fracture and Fatigue Cracking

Tot

al %

T

ask

Com

plet

ed

Q1 Q2 Q3 Q4 Q1Calendar Yr 2008 Calendar Yr 2009

Progress

12

Phase / TaskQuarter

Month 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11

Task A1 – Review of previous studies and available data

10% 10% 2% 0% 2% 6% 30%

Task A2 – Analytical Analysis 12% 2% 4% 0% 18%

Task A3 – Experimental Design, Binder and Agg. Eval.

15% 15% 10% 5% 15% 60%

Task A4 – Prep and Evaluation of Asphalt Mixtures

5% 5% 10% 15% 35%

Task A5 – Data Analysis 10% 10%

Task B1 – Literature Review 10% 15% 5% 5% 5% 5% 45%

Task B2 – Finite Element Analysis 5% 5% 5% 15%

Task B3 – Development of the Fracture Test Procedure

12% 2% 14%

Task B4 – Prep and Evaluation of Asphalt Mixtures

0%

Task B5 – Data Analysis 0%

R1 R2 R3 R4 R5 R6 R7 Final

Calendar Yr 2007

Year 1

Phase A: Evaluation of Mix Resistance to Deformation

Phase B: Evaluation of Mix Resistance to Fracture and Fatigue Cracking

Q1 Q2 Q3Calendar Yr 2008

Phase A – Deformation Studies

13

Literature Review Phase A – Task 1

A. Abu Abdo14

Page 15

Phase A: Task 1 Literature Review

Numerical and Analytical Predictive Models

Voigt(1889)

Reuss(1929)

Hirsch(1962)

Counto(1964)

Hashin(1964)

16

Page 17

Finite Element Modeling

Discrete Element Modeling

Empirical Predictive Models Asphalt Institute Method (Shook and Kallas

(1969)) → only for 4 cps. Refined Witczak Equation (Miller et al. (1983))

→ Bigger range of data. Witczak and Fonesca Model (1996) –

MEPDG level 3 → Modified and Aged Binders and wider range of Temp.

Christensen et al. (2003) Model → G*. Modified Witczak Model (2006) → G* and 𝛿.

18

Factors Affecting the Dynamic Modulus of Asphalt Mixes

Binder

Aggregates

Air voids

(Interaction?)

19

20

Asphalt Binder Properties

1. Viscoity (RV)

2. Asphalt Binder Shear Modulus (G*).

Measured by the Dynamic Shear Rheometer.

21

Aggregates Properties

1. Shape Characteristics

Angularity. Texture. Form/Sphericity.

Measured by AIMS.

Page 22

Aggregates Properties

2. Orientation (Δ).

Measured by: X-Ray tomography. Image Analysis.

2

1

1

2

1

2 )2(sin)2(cos1

M

k

kM

k

k

M

(Isotropy?)

Why Aggregate Orientation (Δ)?

Page 23

(After Masad 2002)

24

Aggregates Properties

3. Structure.

Measured by the Gyratory Stability (GS).

Ndesign

Gyratory Stability, GS = SN . de NG1

Model Development Methodology

Quantify the properties of Aggregates using Image Analysis.

Incorporate these properties in a model to predict HMA Dynamic Modulus (E*).

25

Use of E* for Rutting Prediction

Utilize the actual/predicted E* to evaluate permanent deformation in HMA,

vpveet

Φ

E’ =E*.cosΦ

E”=E*.sinΦ

E**Et

sin.*Evp

cos.*Evee

26

Constitutive Models

Use FEA or DEM Simulation to validate our approach with actual test data.

27

Analytical Analysis – Task A2Finite Element Analysis – Task B2

T. Weaver28

Purpose of Numerical Modeling

Predict E* given aggregate and binder properties

Predict pavement performance and assess influence of multiple variables (loads, environment) on behavior

Comparative assessment of mix designs

29

Numerical Methods

Discrete Element

30

Numerical Methods

Finite Elements

31

Constitutive Models

Hooke’s Law

E

32

Constitutive Models

Viscoelasticity

Viscoplasticity

21

12

2

EE

EE

E ve

ve

1

1

1 mmvpnvp mAq

33

Constitutive Models

Viscoelastoplasticvpve

Parameter

Anisotropy,

Viscoelastic stiffness, E1

Viscoelastic stiffness, E2

Poisson’s ratio,

Drucker-Prager friction angle,

Drucker-Prager cohesion,0

Perzyna’s viscoplastic parameter,

Perzyna’s viscoplastic parameter, N

Dilation Parameter,

Damage Parameter, 1

Damage Parameter,1

Damage Parameter,1

Hardening Parameter, 1

Hardening Parameter, 2

34

Analyses using Viscoplasticity

35

Analyses with Viscoelastoplastic Model

36

Finite Element Analyses

37

Finite Element Analyses

38

Experimental ProgramPhase A – Tasks 3, 4 and 5

F. Bayomy39

Mix Matrix

4 Aggregates Structures (Fine Mix, SP3, SP4 and Coarse Mix).

8 Binders; PG 70-34, PG 70-28, PG 70-22, PG 64-34, PG 64-28, PG 64-22, PG 58-34 and PG 58-28.

7 Field Mixes.

40

PG High Grade

PG Low Grade -34 -28 -22

70

AC% -0.5 Opt 0.5 -0.5 Opt 0.5 -0.5 Opt 0.5

Coarse Mix         √        Mix 1 > 30x106   √   √ √ √   √  Mix 2

3 - 30x106   √     √        

Fine Mix         √        

64

PG Low Grade -34 -28 -22

AC% -0.5 Opt 0.5 -0.5 Opt 0.5 -0.5 Opt 0.5

Mix 1 > 30x106       √        Mix 2

3 - 30x106 √ √ √   √     √  

58

PG Low Grade -34 -28

 

AC% -0.5 Opt 0.5 -0.5 Opt 0.5

Mix 1 > 30x106         √  Mix 2

3 - 30x106   √        

41

Aggregate Gradation

42

Test Setups

SGC

Coring Machine

SPTLVDT Fixture

43

Test Setups

APADSR

AIMS Image Analysis

44

Tests

Binder G*, and Master Curves - Completed

Gyratory Stability (GS) - Completed

E*, and Flow Number (Fn) (In progress)

APA

Image Analysis

45

PG High Grade

PG Low Grade -34 -28 -22

70

AC% -0.5 Opt 0.5 -0.5 Opt 0.5 -0.5 Opt 0.5

Coarse Mix         8        Mix 1 > 30x106   22   13 22 13   22  Mix 2

3 - 30x106   13     13        

Fine Mix         8        

64

PG Low Grade -34 -28 -22

AC% -0.5 Opt 0.5 -0.5 Opt 0.5 -0.5 Opt 0.5

Mix 1 > 30x106       13        Mix 2

3 - 30x106 13 22 13   22     22  

58

PG Low Grade -34 -28

 

AC% -0.5 Opt 0.5 -0.5 Opt 0.5

Mix 1 > 30x106         13  Mix 2

3 - 30x106   13        

46

Field MixesMix PG AC% APA E* GS/Jc/Jc*

1. (Jerome IC) 70-28 4.90% 2 2 32. (Topaz to Lava) 60-34 4.35% 2 2 3

3. (Lapwai to Spalding)

70-28 5.40% 2 2 3

4. (US 95/SH 6) 58-34 6.20% 2 2 35. (US 20) 70-28 5.12% 2 2 36. (SR270) 70-28 5.90% 2 2 3

7. (SR270) 70-28 5.10% 2 2 3

Total 14 14 21

In addition to MnROAD Mixes47

E* Testing is in progress

48

Data Analysis – Task A5

49

Binder Viscosity Results

0.01

0.10

1.00

10.00

130 135 140 145 150 155 160 165 170

Vis

cosi

ty,

Pa

.S

Temperature, oC

PG58-28

PG58-34

PG64-22

PG64-28

PG64-34

PG70-22

PG70-28

PG70-34

50

Binder G* Results

51

52

Phase B – Fatigue and FractureProgress in Tasks B1, 2 and 3

S. Jung53

Medani (2000) shows the results of 108 displacements controlled fatigue tests which were either 3 point or 4 point bending fatigue tests and derives following equation:

Nf : load cycles to failure,

ε : strain amplitude,

Vb: volume percentage of binder in the mix,

Va: volume percentage of air in the mix,

Vg: volume percentage of aggregate in the mix,

PI: Penetration index

Sm: stiffness modulus (in MPa)

Pen: penetration of the bitumen (in 0.10 mm)

VFB: voids in the aggregate skeleton filled with bitumen

= Vb/( Vb+ Va )

δ: Phase angle

Smix: Mix stiffness (MPa)

TR&B: Ring and ball temperature (°C)

m: slope of a master curve

nmas: n-value determined from the master curve

CF: correction factor

BRbbmixab

b TvpivSvv

vnk &1 01366.0log085.304551.0

4146552.1856.3456.1log

n

f kN

1

1

54

H.J. Lee (2002) presents a prediction can be simplified for the fatigue life of asphalt mixes using viscoelastic properties only.

11 2

0

2*)5.0(1 )()(

4

Ea

fN b

f

2

01k

f kN

12*)5.0(11 )(

4

Ea

fk b

mk /212 12

where f = loading frequency

E* = dynamic modulus

a, b

= regression constants

55

Flowchart for Proposed Test Procedure analysis(Daniel 2002)

56

Method comparison

Number of loading is common variables Work with Cyclic loading Estimate fatigue information Work with energy concept

57

Dynamic function

Monotonic function 58

Energy Comparison

Dynamic ApproachFailure Energy (lb.in)

0.21 for actual

0.38 for duplication

0.58 for under curve

Static ApproachNotch Failure Energy

(in) (lb.in)

0.59 5.22

1.03 4.56

1.5 2.32

*** Difference caused the following reasons; sample, peak load, deformation, loading condition (Hz, temperature, etc)

59

MTS systemupgrade

60

Sample preparation

61

Modifying cooling unit

62

Potential study variables

Temperature (°C) Strain rate(10-6 units/s)

Frequency(Hz)Monotonic Cyclic

(Daniel 2002) 5, 20 5, 12, 20 10, 30, 500, 1500 1, 10

(R. Lundstrom 2003) 0,10,20 100, 200, 400, 800 10

(T.O. Medani 2000) 5, 15, 20, 25, 30 for n 10-50

(H. J. Lee 2002) 25 5

Testing plan: plan to test 36 samples for each mix type to understand function of each variable

63

Questions

64