[PPT] - Separating Load from Moisture Effects in Wet Hamburg Wheel-Track PowerPoint Presentation

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1 Quan (Mike) Lv, Ph.D. Hussain Bahia, Ph.D. March 6, 2019 Fort Worth, Texas

School of Transportation Engineering Tongji University

Separating Load from Moisture Effects in Wet Hamburg Wheel-Track Test

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2

Acknowledgments

◆ The financial supports:

➢ WisDOT: Wisconsin Highway Research Program 17-06.

◆ Project Collaborator:

➢

Dr. Preeda Chaturabong

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Outline

Background Materials & Testing Methods Identification of Confounding Effect Proposal of a Novel Analysis Method Validation of the Proposed Method Findings & Conclusions

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Background

Part

01

Background

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5

Background

 The wet HWTD test is widely used to identify asphalt mixes that are prone to rutting and moisture damage (Aschenbrener et al., 1993). ○ Confounding effects of loading and moisture (Lu, 2005; Mohammad et

al. 2015, NCHRP-W219; Tsai et al., 2016; Swiertz et al., 2017).

○ Limited specifics are provided in AASHTO T324-17 for the analysis of results (Mohammad et al., 2017).

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Background

 There is a need to separate Loading effects from Moisture effects  Option 1: Conducting the HWTD test under both dry and wet.

“The moisture sensitivity related performance can be determined by subtracting the rutting response curve of a dry HWTD test from that of a wet HWTD test. ”

Lu, Q. Investigation of conditions for moisture damage in asphalt concrete and appropriate laboratory test methods. University of California Transportation Center, 2005.

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Background

 Option 2

○ Separating Load from Moisture Effects in Wet HWT test. (Yin et al., NCHRP

Project 9-49, 2014)

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Materials & Testing Methods

Part

02

Materials & Testing Methods

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Materials & Testing Methods

Experimental Plan

Eight different mixtures Dry HWT test Proposal of a Modified Analysis Method BBS AASHTO T 361-mastic test Validation of the Proposed Method Wet HWT test Identification of Confounding Effect (initial consolidation, confound effect, existing solution)

Wet HWT test Validation of the normalization procedure Validation of the proposed parameters

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10 ◆ Eight mixture types: 2 aggregate types, 2 traffic levels and 2 binders

Materials

Mixture ID Aggregate Type Traffic Mix Level Binder Type PG 58 C-MT-S28 Cisler (Granite) MT S-28 C-MT-V28 MT V-28 C-HT-S28 HT S-28 C-HT-V28 HT V-28 W-MT-S28 Waukesha (Limestone) MT S-28 W-MT-V28 MT V-28 W-HT-S28 HT S-28 W-HT-V28 HT V-28

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Testing Methods

◆HWT test:

➢ AASHTO T324-17, 50 ± 1 °C

(a) PMW Hamburg Single Wheel Tracker (b) Set up for the dry condition test.

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Testing Methods

◆HWT test:

➢ Iowa DOT analysis method ➢ Creep Slope: CS ➢ Stripping Inflection Point: SIP ➢ Strip Slope: SS

5.0E-03
4.5E-03
4.0E-03
3.5E-03
3.0E-03
2.5E-03
2.0E-03
1.5E-03
1.0E-03
5.0E-04

0.0E+00 1000 2000 3000 4000 5000 6000 7000 8000

Slope of Curve (1st Derivative) Number of Wheel Passes

First Derivative

25
20
15
10
5

1000 2000 3000 4000 5000 6000 7000 8000

Rut Depth (mm) Number of Wheel Passes

HWTD test rutting curve

Measured rut depth Fitted rut depth (6th polynomial)

Creep range Strip range

SIP Creep pass Strip pass

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Testing Methods

◆Binder Bonding Strength (BBS) test:

➢ Based on AASHTO T361

(a) BBS test device (b) the equipment to control the temperature. Loss of POTS =

𝑄𝑃𝑈𝑇𝑒𝑠𝑧−𝑄𝑃𝑈𝑇𝑥𝑓𝑢 𝑄𝑃𝑈𝑇𝑒𝑠𝑧

× 100

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Identification of Confounding Effect

Part

03

Identification of Confounding Effect

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Identification of Confounding Effect

Rutting depths after first 1000 wheel passes are highly correlated to the AV contents.

Rutting depth at first 1,000 passes R² = 0.9162 R² = 0.3525 2000 4000 6000 8000 10000 12000 14000 16000 18000 0.0 1.0 2.0 3.0 4.0 5.0 6.0 6.0 6.5 7.0 7.5 8.0 Number of Passes to Failure (pass) Rutting depth at first 1,000 passes (mm) Air Void (%) Rutting depth at first 1,000 passes NPF

◆ Confounding effect of initial consolidation (First 1000 Cycles)

5.2 mm

(vs. 12.5mm)

There are strong confounding effects of specimen air void and post-compaction consolidation

1.4 mm

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Identification of Confounding Effect

◆ Effects of water conditioning on the creep stage

25
20
15
10
5

5 2000 4000 6000 8000 10000 12000

Rut depth (mm) Number of wheel passes

Rut-Wet(Measured) Rut-Dry (Measured) Rut-Difference=Rut(Wet)-Rut(Dry)

water conditioning enhancement

Surpassing point

“water conditioning enhancement” will affect the CS, SS, SIP.

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Identification of Confounding Effect

◆ Effects of water conditioning on the creep stage

16
14
12
10
8
6
4
2

2 4 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Rut depth (mm) Number of wheel passes

C-HT-V28 C-MT-V28 W-HT-V28 W-MT-V28 C-HT-S28 C-MT-S28 W-HT-S28 W-MT-S28

“water conditioning enhancement” is

bserved in all eight mixtures.

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Identification of Confounding Effect

◆ Existing method to solve the confounding effect (Texas method, NCHRP Project 9-49 )

Assumption: the inflection point

f the curve is an indicator of

the onset of the stripping.

0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05

0.00 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Strain (ɛ) Number of wheel passes Measured wet HWT data Projected viscoplatic strain

𝜁^v𝑞=𝜁_∞^vp 𝑓𝑦𝑞[〖−(𝛽/𝑀𝐷)〗^𝜇 ]

𝜁^st=(−12.5−𝜁^v𝑞)/ (𝑇𝑞𝑓𝑑𝑗𝑛𝑓𝑜 𝑈ℎ𝑗𝑑𝑙𝑜𝑓𝑡𝑡)

Inflection point (LCSN) Stripping life (LCST)

viscoplastic strain increment (∆𝜁_10,000^𝑤𝑞)

Negative Curvature

Positive Curvature

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Identification of Confounding Effect

◆ Existing method to solve the confounding effect (Texas method, NCHRP Project 9-49 )

Two models to fit 2 parts of the trend; before and after inflection point

0.0050
0.0045
0.0040
0.0035
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005

0.0000

2000 4000 6000 8000 10000

Slope of curve (1st Derivative) Number of wheel passes Wet 1st Derivative Dry 1st Derivative

Inflection point

secondary zone

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Identification of Confounding Effect

◆ Using the proposed method to solve the confounding effect (Texas method, NCHRP Project 9-49 ) applied to our data.

y = 151.99x - 9E-05 R² = 0.53

0.001
0.0009
0.0008
0.0007
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
0.000006
0.000005
0.000004
0.000003
0.000002
0.000001

Slope rate at 10,000 passes in Dry HWT (mm/pass)

Predicted viscoplastic strain increment by Texas method (ɛ)

Wet HWT (Texas method) vs. Dry HWT

Correlation - R2 is not high enough.
Need discount the post-compaction.

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Identification of Confounding Effect

Part

04

Proposal of a Novel Analysis Method

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Proposal of a novel analysis method for wet HWT test

 The total rutting depth = the contribution from visco-plastic deformation + the moisture-induced damage.

◆ Assumptions

 But we need to discount the contribution from the post-compaction phase.  The inflection point of the curve (when the curvature changes from negative to

positive. ) is where the water starts to affect.

 Need an easier model and fit method.

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Proposal of a novel analysis method for wet HWT test

Step 1:Fitting of the raw data

1.50E-06
1.00E-06
5.00E-07

0.00E+00 5.00E-07 1.00E-06 1.50E-06 2.00E-06 2.50E-06 3.00E-06 3.50E-06

2000 4000 6000 8000 10000 12000 14000

Second derivative of rutting curve

Number of wheel passes

Inflection point

20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0

0.0 2000 4000 6000 8000 10000 12000 14000

Rutting depth (mm) Number of wheel passes Inflection point Raw data

Negative Curvature

Positive Curvature

Inflection point 𝐅𝐫. 𝟐: RD 𝑂 = 𝑄

1 × 𝑂6 + 𝑄2 × 𝑂5 + 𝑄3 × 𝑂4

+𝑄

4 × 𝑂3 + 𝑄5 × 𝑂2 + 𝑄6 × 𝑂 + 𝑄7

Eq. q.2 :

𝜖2RD 𝑂 𝜖𝑂2

= 30 × 𝑄

1 × 𝑂4 + 20 × 𝑄2 × 𝑂3 +

12 × 𝑄3 × 𝑂2 + 6 × 𝑄

4 × 𝑂 + 2 × 𝑄5 = 0

Fit curve with a sixth-degree polynomial

function. (Eq.1)

The inflection point where the second derivative of the polynomial first reaches zero after first 1,000 passes. (Eq.2)

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Proposal of a novel analysis method for wet HWT test

𝐅𝐫. 𝟒: RD 𝑂′ ∗ = RD 𝑂′ + 1000 − RD 1000

20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0

0.0 2000 4000 6000 8000 10000 12000 14000

Rutting depth (mm) Number of wheel passes Normalized Data Inflection point Raw data

Inflection point

Normalized rutting depth ,〖RD(𝑂^′ )〗^∗ Normalized loading passes, 𝑂^′

Step 2: Normalization of the fitted data

RD 𝑂′ ∗ is the normalized rutting depth, 𝑂′ is the normalized number of loading passes，RD 1000 is the fitted rutting depth at 1,000 passes.

The fitted rutting depth at first 1000 passes should be subtracted from the fitted rutting curve to normalize the

data. (Eq.3)

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Proposal of a novel analysis method for wet HWT test

20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0

0.0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Normalized rutting depth (mm)

Normalized loading passes

Data in negative curvature Inflection point Normalized rutting depth Projected visco-plastic deformation 12.5 mm

NPF 〖𝑆𝐸〗_𝑔𝑗𝑜𝑏𝑚^𝑤𝑞 〖𝑆𝐸〗_𝑔𝑗𝑜𝑏𝑚^𝑛

〖RD^vp (𝑂^′ )〗^∗ 〖"RD" (𝑂^′ )〗^∗

Step 3: Calculation of the performance-related parameters

Overall performance evaluation: Number of Passes to

Failure (12.5mm, NPF) or maximum Rutting Depth (𝑆𝑣𝑢𝑛𝑏𝑦).

Rutting resistance evaluation:

Visco-plastic Ratio (VR)

RDvp 𝑂′ ∗ = 𝑏 × (𝑂′)𝑊𝑆

Moisture resistance evaluation:

Moisture Ratio (MR)

MR = RDfinal

m

RDfinal

m

+ RDfinal

vp

× 100

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Validation of the Proposed Method

Part

05

Validation of the Proposed Method

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Validation of the Proposed Method

◆ Validation of the normalization procedure

25
20
15
10
5

2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Rutting depth (mm)

Number of wheel passes Original rutting curve in dry HWT test Original rutting curve in wet HWT test Modeled viscoplatic rutting-Texas method

20
18
16
14
12
10
8
6
4
2

2000 4000 6000 8000 10000 12000 14000 16000 18000

Normalized rutting depth (mm)

Normalized loading passes Negative curvature part of normalized wet rutting curve Normalized rutting curve in the wet HWT test Normalized rutting curve in the dry HWT test Modeled visco-plastic deformation-New method

Creep stage Negative curvature

(a) Current method no normalization (b) New method after normalization

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Validation of the Proposed Method

◆ Validation of the normalization procedure

18
16
14
12
10
8
6
4
2

2 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Rut depth (mm) Number of wheel passes

C-HT-V28 C-MT-V28 W-HT-V28 W-MT-V28 C-HT-S28 C-MT-S28 W-HT-S28 W-MT-S28

“water conditioning enhancement” is reduced.

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Validation of the Proposed Method

◆ Evaluating the Post Compaction Phase Limits

Assumption: the post-compaction phase is the first 1,000 passes in the wet HWT test . The point at where the slope is decreased to 80% or 50% of its initial value.

0.0050
0.0045
0.0040
0.0035
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005

0.0000 1000 2000 3000 4000 5000 6000 Slope of curve (1st Derivative)

Number of wheel passes Wet 1st Derivative

Inflection point 50% Initial slope Initial slope 80% Initial slope

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Validation of the Proposed Method

◆ Evaluating the Post Compaction Phase Limits

Reasonable to define the post-compaction range as the first 1,000 passes.
There are enough data between 1,000 passes to the inflection point that can

be used to build the visco-plastic rutting model.

Mixture type Inflection point (pass) passes to 80% initial slope (pass) passes to 50% initial slope (pass) C-HT-S28 2,100 250 700 C-MT-S28 1,850 200 600 C-HT-V28 4,300 450 1350 C-MT-V28 4,300 450 1450 W-HT-S28 2,000 300 1050 W-HT-V28 4,760 3,000 Not reached W-MT-S28 1,800 250 850 W-MT-V28 2,600 300 850 Average 2,964 650 979

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Validation of the Proposed Method

◆ Validation of the proposed parameters

This different ranking confirms that the initial consolidation affects the calculated

parameters in the current analysis method and thus should be discounted.

MR parameter can be used to evaluate the sensitivity of mixtures.

Mixtures NPF-new (pass) RDfinal

vp

(mm) RDfinal

m

(mm) VR (log mm/ log pass) MR (%) NPF-Iowa (pass) C-HT-S28 6320

8.7
3.8

0.89 30.4 (rutting sensitive) 6377 C-MT-S28 4300

8.1
4.4

0.95 34.9 4073 C-MT-V28 13400

6.9
5.6

0.75 45.0 11892 C-HT-V28 16300

4.0
8.5

0.74 67.6 16143 W-HT-S28 5300

5.3
7.2

0.92 57.6 5395 W-HT-V28 13000

3.9
8.6

0.79 69.0 13172 W-MT-S28 4560

5.8
6.7

0.94 53.9 4774 W-MT-V28 9700

3.2
9.3

0.83 74.6 (moisture sensitive) 10093

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y = 0.7874x + 0.1634 R² = 0.96

0.70 0.75 0.80 0.85 0.90 0.95 0.70 0.75 0.80 0.85 0.90 0.95 1.00

VR determined from the dry HWT test (log mm/ log pass) VR determined from the wet HWT test (log mm/ log pass)

y = 1.2173x + 1.1041 R² = 0.87

12
10
8
6
4
2
10
8
6
4
2

Measured rutting depth in dry HWT test (mm) Predicted from the wet HWT test (mm) 〖𝑆𝐸〗_𝑔𝑗𝑜𝑏𝑚^𝑤𝑞

Validation of the Proposed Method

◆ Validation of the proposed parameters

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Validation of the Proposed Method

◆ Validation of the new parameters: Moisture Effects are related to Adhesion

Groups Current parameters BBS test SS (mm/pass) SIP (pass) SS/CS Loss of Adhesion POTS (%) Value Rank Value Rank Value Rank Value Rank C-HT-S28

0.0052

B 6189 A 4.33 C 16.85 A C-MT-S28

0.0059

C 4549 C 2.68 A 30.09 B W-HT-S28

0.0045

A 4685 B 3.46 B 33.73 C Groups Texas method New method LCSN (pass) LCST (pass) MR (%) Value Rank Value Rank Value Rank C-HT-S28 1800 A 6300 A 30.4 A C-MT-S28 1400 B 4700 C 34.9 B W-HT-S28 1400 B 5500 B 57.6 C

Loss of Adhesion Can Explain The MR

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Findings & Conclusions

Part

06

Findings & Conclusions

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Findings and Conclusions

◆ The rutting depths at the first 1,000 wheel passes are very sensitive to the AV contents

f the specimen.

➢ This initial consolidation should be discounted if the interest is in shear deformation rutting. ◆ After eliminating the post-compaction stage, fitting of a simple power-law model allows effective procedure for separating the visco-plastic response due to loading from the moisture effects.

➢ Visco-plastic Ratio (VR), the power factor in the rutting modeling, is proposed to characterize the mixture’s rutting resistance under dry conditions; ➢ Moisture Ratio (MR), the percentage of the moisture-induced deformation in the final rutting depth, is recommended as a moisture resistance parameter and can be used to indicate the damage sensitivity of the mixture.

➢ More work is needed to verify this method, especially the comparison with the field performance.

SLIDE 36

Effects of Modifiers and Binder Properties on the Performance of Asphalt Mixtures in the Hamburg Wheel-Tracking Device Test

Quan Lv (Mike) Ph.D. Candidate 1991Lvquan@tongji.edu.cn March 19, 2018 Jacksonville, FL

School of Transportation Engineering Tongji University

AAPT 94rd Annual Meeting