Infrastructure Renewal Assessment Tools: Pavement Assessment using the Falling Weight Deflectometer Pittsburgh Transportation Forum -April 5, 2017-
Julie M. Vandenbossche, PE, PhD Kevin Alland Nate Bech
University of Pittsburgh Department of Civil and Environmental Engineering
Infrastructure Renewal
Transportation Infrastructure
Nationally: • 20% of highways are in poor condition, (32% of urban roads) • 9% of bridges are structurally deficient • 40% of busses are in marginal or poor condition • 20% of airline flights are delayed • $2 Trillion needed over 10 years to restore/maintain surface transportation system (Data from 2017 ASCE Infrastructure Report Card)
University of Pittsburgh Department of Civil and Environmental Engineering
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Infrastructure Renewal
mljom[l ’ Transportation Infrastructure
Nationally: • 20% of highways are in poor condition, (32% of urban roads) • 9% of bridges are structurally deficient • 40% of busses are in marginal or poor condition • 20% of airline flights are delayed • $2 Trillion needed over 10 years to restore/maintain surface transportation system (Data from 2017 ASCE Infrastructure Report Card)
University of Pittsburgh Department of Civil and Environmental Engineering
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Timely Interventions
(adapted from Federal Highway Administration) University of Pittsburgh Department of Civil and Environmental Engineering
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Timely Interventions
(adapted from Federal Highway Administration) University of Pittsburgh Department of Civil and Environmental Engineering
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Timely Interventions
(adapted from Federal Highway Administration) University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement Evaluation Early/unneeded preservation/rehabilitation results in unnecessary‌.
& Solution: Non Destructive Testing (NDT) University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement Evaluation Performing the right fix at the right time is essential for maintaining pavements in an efficient and cost effective manner.
Solution: Non Destructive Testing (NDT)
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Pavement NDT
Load Plate
Falling Weight Deflectometer (FWD) University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement NDT
Load Plate
Falling Weight Deflectometer (FWD) University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement Evaluation • Establish structural condition • Identify needed repairs • Define design inputs (PavementInteractive.org)
University of Pittsburgh Department of Civil and Environmental Engineering
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Structural Condition Life Cycle Costs ($)
Optimum Under Design
Over Design
Total Lifecycle Cost
Maint./Rehab Cost Overlay Cost Overlay Capacity (Federal Highway Administration)
FWD can help determine optimum design of overlays University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement Evaluation Rigid Pavements • Establish structural condition • Identify needed repairs • Define design inputs (PavementInteractive.org)
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FWD Test Locations
• Joint Performance • Void Detection • k-value
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Joint Performance Poor load transfer • Faulting • Voids • Corner breaks
(PavementInteractive.org) University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance
• Joint Performance • Void Detection • k-value
University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance ΔL = x ΔU = 0
LTE = 0% (Poor) High Diff. Defl
ΔL = x
Load transfer efficiency (LTE LTE = ΔU / ΔL *100% Differential Deflection Diff. Defl = ΔL - ΔU
ΔU = x
LTE = 100% (Good) Diff. Defl. = 0
University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance Assess need for dowel bar retrofits: • Poor load transfer (< 50–60%) • Differential deflections > 0.01 in • Faulting: 0.12–0.5 in
(PavementInteractive.org) University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance - Pitt
University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance- Pitt
Negative Temp. & Moist. Gradient
Positive Temp. Gradient
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Joint Performance- Pitt Socketing
Flat Slab
Negative Gradient
Positive Gradient
Negative and positive curvature â&#x20AC;&#x153;lockâ&#x20AC;? in dowel increasing LTE
Negative Gradient Positive Gradient University of Pittsburgh Department of Civil and Environmental Engineering
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Void Detection
• Joint Performance • Void Detection • k-value
University of Pittsburgh Department of Civil and Environmental Engineering
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Void Detection Corner Break
Direction of Travel
Approach slab
Saturated support layer
Leave slab Joint or crack Void
Movement of water and fines
Wedge of â&#x20AC;&#x153;injected â&#x20AC;&#x153; fines
University of Pittsburgh Department of Civil and Environmental Engineering
(infopave.com)
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Void Detection
Fill with grout or polyurethane All overlay designs in Pavement ME assume the slab is fully supported.
University of Pittsburgh Department of Civil and Environmental Engineering
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Current Methods Peak Deflection
Normalized 9kip Deflection
Normalized 9kip Deflection
Compare Deflections
Joint Along Project
FWD Load (kips)
ELTG
Void Parameter (VP)
(oF/in)
Corner Deflection (mils) University of Pittsburgh Department of Civil and Environmental Engineering
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Void Detection 35
Max Deflect. (mils)
30
11/2/2010 Night, Avg Air Temp 56 ยบF
25 20
Void Cutoff
4/14/2011 Day, Avg. Air Temp 68 ยบF
15 10 5
4/19/2011 Night, Avg. Air Temp 51 ยบF
0 0
500
1000 Offset (ft)
1500
2000
I-79 FWD Testing 2010-2011 (Ramirez 2011) University of Pittsburgh Department of Civil and Environmental Engineering
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Joint Performance- Pitt Potential for False Positive
Negative Temp. & Moist. Gradient
Potential for False Negative
Positive Temp. Gradient
University of Pittsburgh Department of Civil and Environmental Engineering
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Void Detection-Pitt I-79, Bridgeville, PA
University of Pittsburgh Department of Civil and Environmental Engineering
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Void Detection - Pitt Void Prediction Tool â&#x20AC;˘ TempCurv: CTE*ELTG â&#x20AC;˘ LTE: in the wheelpath, from NDT
â&#x20AC;˘ Total Deflection: normalized deflection at 9,000 lbs â&#x20AC;˘ Void Parameter: intercept of regression line â&#x20AC;˘ Deflection Ratio (DR) đ??ˇđ?&#x2018;&#x2026; = đ??ˇđ??šđ?&#x2018;&#x160;đ??ˇ,9đ?&#x2018;&#x2DC; /đ??ˇđ?&#x2018;&#x2021;â&#x201E;&#x17D;đ?&#x2018;&#x2019;đ?&#x2018;&#x153;,9đ?&#x2018;&#x2DC; DFWD,9k = Measured deflection for 9 kip FWD load DTheo,9k = Calculated deflection for 9 kip load
University of Pittsburgh Department of Civil and Environmental Engineering
Void Detection - Pitt
University of Pittsburgh Department of Civil and Environmental Engineering
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Pavement Evaluation Rigid Pavements • Establish structural condition • Identify needed repairs • Define design inputs Bonded Concrete Overlay
Concrete Pavement Restoration Existing Pavement Existing Stabilized Base
Unbonded Concrete Overlay Overlay Existing Pavement Existing Stabilized Base
Epcc and k-value
University of Pittsburgh Department of Civil and Environmental Engineering
Overlay Existing Pavement Existing Stabilized Base
Asphalt Overlay Overlay Existing Pavement Existing Stabilized Base
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Design Inputs PCC stiffness (Epcc) and modulus of subgrade reaction (k-value): • Concrete pavement restoration k-value • Bonded concrete overlays • Unbonded concrete overlays • Asphalt overlays
Epcc, Unbonded overlay
Epcc, Bonded & AC overlay & Restoration
University of Pittsburgh Department of Civil and Environmental Engineering
Backcalculate Design Inputs Stiffness = f (materials and shape) P
h b
δ PL3 δ= 48EI
L/2
I=
bh3 12
L University of Pittsburgh Department of Civil and Environmental Engineering
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Design Inputs
• Joint Performance • Void Detection • Epcc and k-value
University of Pittsburgh Department of Civil and Environmental Engineering
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Design Inputs -Pitt
Positive gradient Positive gradient
PCC Elastic Modulus (I-79)
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k-Value (I-79)
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Design Inputs-Pitt
Positive Gradient
Reduced support
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Pavement Evaluation Flexible Pavements â&#x20AC;˘ Define design inputs
(PavementInteractive.org)
University of Pittsburgh Department of Civil and Environmental Engineering
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Design Inputs Overlay: • Layer thicknesses • Stiffness • Asphalt concrete • Base • Subgrade • Overlay mixture design Asphalt concrete overlay (Texas A&M Transportation Institute)
University of Pittsburgh Department of Civil and Environmental Engineering
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FWD Test Location
Outside Wheelpath
â&#x20AC;˘ Backcalculate layer stiffnesses
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Design Inputs Overlay: • Layer thicknesses • Stiffness • Asphalt concrete • Base • Subgrade • Overlay mixture design Asphalt concrete overlay (Texas A&M Transportation Institute)
University of Pittsburgh Department of Civil and Environmental Engineering
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Factors Influencing Asphalt Stiffness • Load Frequency
Increase Stiffness
Mastercurve
• Temperature
Increase Stiffness
• Aging –Increase stiffness • Fatigue- Decreases stiffness University of Pittsburgh Department of Civil and Environmental Engineering
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Dynamic Modulus (E*) Master Curve 3500 3000
14 F
2500
40 F
2000 70 F
1500 Log(a(40)) = 2.10
1000 Log(a(130)) = -3.57
100 F
Shift = log(a(T))
Dynamic Modulus (E*) (ksi)
Log(a(14)) = 4.14
Log(a(100)) = -1.88
130 F
500 0 0.00001
5 4 3 2 1 0 -1 -2 -3 -4 -5
Shift Factors
0 0.01
10
10000
Load Frequency (Hz)
University of Pittsburgh Department of Civil and Environmental Engineering
50
100
Temperature (F)
42
150
Determining E* 1. Measure E* on cores
2. Witzcak equation â&#x20AC;˘ Mixture parameters (use cores)
3. FWD testing
(Pavement Interactive)
Dynamic Modulus (E*) (ksi)
3000 2500 2000 1500 1000 500 0 (American Engineering Testing)
20
Asphalt core sample
University of Pittsburgh Department of Civil and Environmental Engineering
70 Temperature (oF)
43
120
Laboratory E* Testing Perform E* testing on cores
Dynamic Modulus (E*) (ksi)
3000 2500 2000 1500 1000 500 0
Laboratory dynamic modulus test Asphalt core sample
20
40
60
80
100
Temperature (oF)
(American Engineering Testing)
University of Pittsburgh Department of Civil and Environmental Engineering
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120
Witczak Equations Estimate E* from mix parameters of sample
â&#x20AC;˘ â&#x20AC;˘ Mix parameters from core sample
Determine mixture parameters from cores Use mixture parameters to estimate variables in Witcak equation
Dynamic Modulus (ksi)
3000
University of Pittsburgh Department of Civil and Environmental Engineering
2500 2000 1500
1000 500 0 20
40
60
80
Temperature
100
(oF)
45
120
FWD Testing â&#x20AC;˘ Perform FWD testing â&#x20AC;˘ Backcalculate stiffness of existing asphalt 3000
FWD Testing (Cornell Local Roads Program)
FWD Load
Deflection Sensors
Modulus (ksi)
2500 2000 1500 1000
500 0
Calculated Deflection Basin
20
70
120
Temperature (oF)
Measured Deflection Basin
Backcalculation (Pavement Interactive)
University of Pittsburgh Department of Civil and Environmental Engineering
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Comparison of E* Estimates -Pitt 3000 Dynamic Modulus (E*) from Coring and Lab Testing
Modulus (ksi)
2500 2000
Dynamic Modulus (E*) from Coring and Predictive Equation
1500 1000 500
Elastic Modulus (E ) from Backcalculation
0 20
40
60
80
100
120
Temperature (oF) University of Pittsburgh Department of Civil and Environmental Engineering
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Challenges â&#x20AC;˘ Do all methods provide the same mastercurve? â&#x20AC;˘ If not, what E* provides the best estimate of performance in PA? â&#x20AC;˘ Best approach for using FWD testing to establish E* 3000
Modulus (ksi)
2500 2000 1500
1000 500 0 20
70
120
Temperature (oF) University of Pittsburgh Department of Civil and Environmental Engineering
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Predicted Performance - Pitt MnROAD Cell 21 Asphalt Thickness = 8 in
Design Life = 20 years AADTT = 750
Distress Predicted using Pavement ME
Maximum Allowable
Dynamic Modulus from Coring & Testing E* in Lab
Dynamic Modulus from Coring and Predictive Equation
Modulus from FWD Testing
Total Rutting (in)
0.75
0.87
0.75
0.91
AC Rutting (in)
0.25
0.32
0.22
0.35
1000
2029
1034
2029
25.00
1.98
1.99
2.02
2000
1011
456
1159
AC Thermal Cracking (ft/mi) AC Bottom-up cracking (%) AC Top-down Cracking (ft/mi)
Which best reflects actual performance?? University of Pittsburgh Department of Civil and Environmental Engineering
Conclusions • FWD testing can enhance infrastructure renewal by: • Aiding in selecting pavement restoration/preservation techniques • Developing more accurate design inputs
• FWD testing on rigid pavements is affected by curling and warping • Methods to minimize errors due to curling and warping are being developed
• E* for asphalt can be determined using FWD, E* lab testing, and predictive models • Effect of using each method on overlay design is being evaluated • Approach for establishing design inputs using FWD testing being developed University of Pittsburgh Department of Civil and Environmental Engineering
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Acknowledgements PennDOT • Project funding • Providing FWD testing and traffic control Bill Dipner, Tom Adams, Theresa Thompson, Steve Nixon, Lydia Peddicord and Josh Freeman
MnDOT • Providing extensive MnROAD FWD and instrumentation data Ben Worel, Tom Burnham, and Dave Van Deusen
University of Pittsburgh Department of Civil and Environmental Engineering
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Thank You
Any Questions?
University of Pittsburgh Department of Civil and Environmental Engineering