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

Separating Load from Moisture Effects in Wet Hamburg Wheel-Track Test Quan (Mike) Lv, Ph.D. Hussain Bahia, Ph.D. March 6, 2019 Fort Worth, Texas School of Transportation Engineering Tongji University 1

Acknowledgments ◆ The financial supports: ➢ WisDOT: Wisconsin Highway Research Program 17-06. ◆ Project Collaborator: ➢ Dr. Preeda Chaturabong 2

Outline  Background  Materials & Testing Methods  Identification of Confounding Effect  Proposal of a Novel Analysis Method  Validation of the Proposed Method  Findings & Conclusions 3

Background Part Background 01 4

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). 5

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. 6

Background  Option 2 ○ Separating Load from Moisture Effects in Wet HWT test. (Yin et al., NCHRP Project 9-49, 2014) 7

Materials & Testing Methods Part Materials & Testing Methods 02 8

Materials & Testing Methods Experimental Plan Eight different mixtures Dry HWT test Wet HWT test BBS AASHTO T Identification of Confounding Effect 361-mastic test (initial consolidation, confound effect, existing solution) Proposal of a Modified Analysis Method Wet HWT test Validation of the Proposed Method Validation of the proposed Validation of the normalization parameters procedure 9

Materials ◆ Eight mixture types: 2 aggregate types, 2 traffic levels and 2 binders Traffic Binder Type Mixture ID Aggregate Type Mix Level PG 58 C-MT-S28 MT S-28 C-MT-V28 Cisler MT V-28 C-HT-S28 (Granite) HT S-28 C-HT-V28 HT V-28 W-MT-S28 MT S-28 W-MT-V28 Waukesha MT V-28 W-HT-S28 (Limestone) HT S-28 W-HT-V28 HT V-28 10

Testing Methods ◆ HWT test: ➢ AASHTO T324-17, 50 ± 1 °C (a) PMW Hamburg Single Wheel Tracker (b) Set up for the dry condition test. 11

Testing Methods HWTD test rutting curve Number of Wheel Passes 0 1000 2000 3000 4000 5000 6000 7000 8000 0 ◆ HWT test: Measured rut depth Fitted rut depth (6th polynomial) -5 SIP Rut Depth (mm) ➢ Iowa DOT analysis method -10 -15 ➢ Creep Slope: CS Creep -20 range ➢ Stripping Inflection Point: SIP -25 First Derivative ➢ Strip Slope: SS Number of Wheel Passes 0 1000 2000 3000 4000 5000 6000 7000 8000 0.0E+00 -5.0E-04 Strip Slope of Curve (1st Derivative) -1.0E-03 range -1.5E-03 -2.0E-03 Creep pass -2.5E-03 -3.0E-03 -3.5E-03 -4.0E-03 -4.5E-03 -5.0E-03 12 Strip pass

Testing Methods ◆ Binder Bonding Strength (BBS) test: ➢ Based on AASHTO T361 Loss of POTS = 𝑄𝑃𝑈𝑇 𝑒𝑠𝑧 −𝑄𝑃𝑈𝑇 𝑥𝑓𝑢 × 100 𝑄𝑃𝑈𝑇 𝑒𝑠𝑧 (a) BBS test device (b) the equipment to control the temperature. 13

Identification of Confounding Effect Part Identification of Confounding Effect 03 14

Identification of Confounding Effect ◆ Confounding effect of initial consolidation (First 1000 Cycles) Rutting depth at first 1,000 passes NPF Rutting depth at first 1,000 passes (mm) Rutting depths after first 1000 18000 6.0 Number of Passes to Failure (pass) R² = 0.3525 5.2 mm 16000 wheel passes are highly 5.0 (vs. 12.5mm) 14000 correlated to the AV contents. 12000 4.0 10000 There are strong confounding 3.0 8000 effects of specimen air void 6000 2.0 1.4 mm and post-compaction 4000 Rutting depth at first 1,000 passes 1.0 consolidation 2000 R² = 0.9162 0 0.0 6.0 6.5 7.0 7.5 8.0 Air Void (%) 15

Identification of Confounding Effect ◆ Effects of water conditioning on the creep stage 5 Rut-Difference=Rut(Wet)-Rut(Dry) Number of wheel passes water conditioning enhancement 0 0 2000 4000 6000 8000 10000 12000 -5 Rut depth (mm) “water conditioning Rut-Wet(Measured) -10 enhancement” will affect the CS, SS, SIP. -15 Surpassing point -20 Rut-Dry (Measured) -25 16

Identification of Confounding Effect ◆ Effects of water conditioning on the creep stage C-HT-V28 C-MT-V28 W-HT-V28 W-MT-V28 C-HT-S28 C-MT-S28 W-HT-S28 W-MT-S28 4 2 Number of wheel passes 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 -2 Rut depth (mm) “water conditioning enhancement” is -4 observed in all eight mixtures. -6 -8 -10 -12 -14 -16 17

Identification of Confounding Effect ◆ Existing method to solve the confounding effect (Texas method, NCHRP Project 9-49 ) Number of wheel passes Inflection point (LC SN ) 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Assumption: the inflection point 0.00 viscoplastic strain increment of the curve is an indicator of ( ∆ 𝜁 _10,000^ 𝑤𝑞 ) Negative -0.05 Curvature 𝜁 ^v 𝑞 = 𝜁 _∞^ vp the onset of the stripping. -0.10 Positive Curvature 𝑓𝑦𝑞 [ 〖−( 𝛽 / 𝑀𝐷 ) 〗 ^ 𝜇 ] -0.15 Strain (ɛ) Stripping life (LC ST ) -0.20 -0.25 𝜁 ^st =(−12.5− 𝜁 ^v 𝑞 )/ -0.30 ( 𝑇𝑞𝑓𝑑𝑗𝑛𝑓𝑜 𝑈 ℎ 𝑗𝑑𝑙𝑜𝑓𝑡𝑡 ) Measured wet HWT data -0.35 Projected viscoplatic strain -0.40 18

Identification of Confounding Effect ◆ Existing method to solve the confounding effect (Texas method, NCHRP Project 9-49 ) Number of wheel passes 0 2000 4000 6000 8000 10000 0.0000 Two models to fit 2 parts of the secondary zone -0.0005 Slope of curve (1st Derivative) trend; before and after inflection -0.0010 Wet 1st Derivative -0.0015 point Dry 1st Derivative -0.0020 Inflection point -0.0025 -0.0030 -0.0035 -0.0040 -0.0045 -0.0050 19

Identification of Confounding Effect ◆ Using the proposed method to solve the confounding effect (Texas method, NCHRP Project 9-49 ) applied to our data. -0.001 Slope rate at 10,000 passes in Dry HWT y = 151.99x - 9E-05 -0.0009 R² = 0.53 -0.0008 -0.0007 Wet HWT (Texas method) vs. Dry HWT -0.0006 (mm/pass) -0.0005 • Correlation - R 2 is not high enough. -0.0004 • Need discount the post-compaction. -0.0003 -0.0002 -0.0001 0 0 -0.000001 -0.000002 -0.000003 -0.000004 -0.000005 -0.000006 Predicted viscoplastic strain increment by Texas method (ɛ) 20

Identification of Confounding Effect Part Proposal of a Novel Analysis Method 04 21

Proposal of a novel analysis method for wet HWT test ◆ Assumptions  The total rutting depth = the contribution from visco-plastic deformation + the moisture-induced damage.  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. 22

Number of wheel passes Proposal of a novel analysis method for wet HWT 0 2000 4000 6000 8000 10000 12000 14000 0.0 -2.0 Step 1:Fitting of the raw data test -4.0 Positive Rutting depth (mm) Curvature -6.0 Fit curve with a sixth-degree polynomial -8.0 function. (Eq.1) Negative Inflection -10.0 Curvature point -12.0 The inflection point where the second -14.0 Inflection point derivative of the polynomial first reaches -16.0 Raw data -18.0 zero after first 1,000 passes. (Eq.2) -20.0 3.50E-06 1 × 𝑂 6 + 𝑄 2 × 𝑂 5 + 𝑄 3 × 𝑂 4 3.00E-06 𝐅𝐫. 𝟐: RD 𝑂 = 𝑄 Second derivative of rutting curve 4 × 𝑂 3 + 𝑄 5 × 𝑂 2 + 𝑄 6 × 𝑂 + 𝑄 7 2.50E-06 +𝑄 2.00E-06 1.50E-06 𝜖 2 RD 𝑂 1 × 𝑂 4 + 20 × 𝑄 2 × 𝑂 3 + Eq. q.2 : = 30 × 𝑄 1.00E-06 𝜖𝑂 2 Inflection point 12 × 𝑄 3 × 𝑂 2 + 6 × 𝑄 4 × 𝑂 + 2 × 𝑄 5 = 0 5.00E-07 Number of wheel passes 0.00E+00 0 2000 4000 6000 8000 10000 12000 14000 -5.00E-07 -1.00E-06 -1.50E-06 23

Proposal of a novel analysis method for wet HWT test Step 2: Normalization of the fitted data Number of wheel passes The fitted rutting depth at first 1000 0 2000 4000 6000 8000 10000 12000 14000 0.0 passes should be subtracted from the Normalized loading passes, 𝑂 ^ ′ -2.0 fitted rutting curve to normalize the Normalized rutting depth , 〖 RD( 𝑂 ^ ′ )〗 ^ ∗ Rutting depth (mm) -4.0 data. (Eq.3) -6.0 -8.0 Inflection -10.0 point 𝐅𝐫. 𝟒: RD 𝑂 ′ ∗ -12.0 = RD 𝑂 ′ + 1000 − RD 1000 -14.0 Normalized Data -16.0 Inflection point -18.0 Raw data -20.0 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. 24