pavement deflections – past, present, and...
TRANSCRIPT
TRANSPORTATION RESEARCH BOARD
@NASEMTRB#TRBwebinar
Pavement Deflections –Past, Present, and Future
June 24, 2020
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Learning Objectives
#TRBwebinar
1. Identify current pavement deflection testing methods and evaluation procedures
2. Describe the historical background of pavement deflection testing devices and methods
Overview of Pavement NDT Devices and Evaluation Methods
Historical Overview of Pavement Non-Destructive Testing Devices and Evaluation Methods
by
Tom Scullion PE. Texas A&M Transportation InstituteRoberto Trevino-Flores PE. Texas Department of Transportation
Overview of Pavement NDT Devices and Evaluation Methods
Presentation Overview
1. DevicesFrom Benkelman Beam to early generation Rolling Deflectometers to latest generation FWD
2. Evaluation Methods– FWD interpretation schemes for Flexible Pavements
(Bowl Parameters to Linear Elastic backcalculation schemes)– Lessons Learned– Case Studies (Segmentation and Forensics)
2
Overview of Pavement NDT Devices and Evaluation Methods
Benkelman Beam (WASHO 1952)
3
Measures Pavement Rebound
Low cost ($1800.00) widely usedSlow and labor intenseDoes not provide a deflection bowl
Overview of Pavement NDT Devices and Evaluation Methods
Automated Beam La Croix Deflectograph 1978
Overview of Pavement NDT Devices and Evaluation Methods
Curviameter Belgium Road Research Lab 1983
• Operates at around 20 mph
• Geophones embedded in chain
• Generates a deflection bowl every 5 meters,
Overview of Pavement NDT Devices and Evaluation Methods
TxDOT’s Rolling Dynamic Deflectometer - Project Level Testing
Speed 2 mph
Deflection every 2 inches on 3 Geophones
Targeted for Concrete Pavements• LTE• Poor support area• Pavement Acceptance
Overview of Pavement NDT Devices and Evaluation Methods
Dynaflect (TTI: F. Scrivener and G. Swift 1975)
§ Dynaflect
7
Early Backcalculation Methodology (1978)
• Computed Layer Stiffness coefficients• Input in TxDOT FPS 11 design system
Overview of Pavement NDT Devices and Evaluation Methods
Falling Weight Deflectometer (1982)
Weights lifted then dropped
Load PlateDeflection Sensors
8
Overview of Pavement NDT Devices and Evaluation Methods
Traditional and latest Generation FWD in Operation
9
High Speed new generation FWD1 Mile in 8 minutes at 0.1 interval
Overview of Pavement NDT Devices and Evaluation Methods
Uses of FWD data in Flexible Pavement Evaluation and Design
§ Structural Evaluation– Remaining Life
§ Forensics Investigations– Identify the weak layer causing premature pavement failures
§ Pavement Design– Backcalculation of layer moduli values
– Key input into pavement design
§ Pavement Rehabilitation Studies– Project segmentation
10
Overview of Pavement NDT Devices and Evaluation Methods
Some of the Key Developers of FWD analysis tools on Flexible Pavement
§ Jacob Uzan - Technion Israel
– Developed the search engine in MODULUS and JULEA
§ Per Ullidtz – Dynatest
– Developed the widely used ELMOD
§ Lynne Irwin – Cornel University
– FWD Calibration protocols
– MODCOMP developer
§ Al Bush – US Army COE
– Importance of Depth to a stiff layer in matching FWD and Lab moduli values
– WESDEF – lead developer
§ Marshall Thompson– University of Illinois
– AREA method
11
Overview of Pavement NDT Devices and Evaluation Methods
FWD Raw Data
13
Miles GPS 7 Deflections OperatorLoad Temps Comments
Overview of Pavement NDT Devices and Evaluation Methods
Stress Distribution and Deflections under FWD Loading
6/23/2020
CL
Surface
Base
Subgrade
14
Overview of Pavement NDT Devices and Evaluation Methods
Raw Deflection Data Indices
15
W1W2
W3
W7
SCI
BCI
Indices:W1 à Overall Pavement Stiffness
W1-W2 (SCI) à Top 8”
W2-W3 (BCI)à 8” to 16”
W7à Subgrade > 48”
Normalized to 9 kip Drop Load
Overview of Pavement NDT Devices and Evaluation Methods
Simple Pavement Diagnosis based on Bowl parameters
16
Overview of Pavement NDT Devices and Evaluation Methods
Hoffman and Thompson’s Area
17
Overview of Pavement NDT Devices and Evaluation Methods
Back Calculation of Layer Moduli values
18
Forward Calculation
Input layer Thicknesses Output Pavement DeflectionsInput layers Elastic ModuliInput layers Poisson ratioInput applied load and plate size
Back Calculation
Input layer Thicknesses Output Layer Moduli valuesInput Measured DeflectionsInput applied load and Plate sizeInput layer Poisson ratio
Linear Elastic program
Search routineMinimizing Error between measured and computed bowls
Overview of Pavement NDT Devices and Evaluation Methods
Back Calculation of Layer Moduli values
19
Backcalculation is not a mathematical error minimization problem. It is an engineering evaluation tool to arrive at realistic layer MODULI values which can be used for Pavement Design or layer diagnostics
A match between Measured and Computed bowls of less than 2% is worthless if the layer moduli values are way off
Overview of Pavement NDT Devices and Evaluation Methods
Key Issues based on Texas Experience§ Thin layers make it difficult to come up with realistic moduli values
– Hot Mix layers less than 3 inches (Fixed Modulus based on Temp)
– Base Layers less than 6 inches
§ Required Layer thicknesses (critical, unknown and often very variable)
– GPR
§ Matching Lab Moduli values
– Include a Depth to a stiff layer (COE uses 240 inches or MODULUS extrapolate to zero deflection)
§ Using too many layers can be problematic
– 3 layers works fine
– 4 layers can be problematic
– More that 4 layers good luck matching reality
§ Need for temperature correction (HMA moduli)
§ Need for validation testing
– DCP testing
20
Overview of Pavement NDT Devices and Evaluation Methods
Temperature Correction Factor to get E 77F
21
Temperature Correction Factor
0
0.5
1
1.5
2
2.5
3
3.5
50 60 70 80 90 100 110Temp (F)
Fact
orCF = T^2.81/200,000
Overview of Pavement NDT Devices and Evaluation Methods
GPRGround-Penetrating Radar
FWDFalling Weight Deflectometer
DCP
Used extensively for selecting rehab options when pavement performance is poor or premature failures exist
TxDOT’s Nondestructive Test (NDT) Evaluation Tools
DCPDynamic Cone Penetrometer
23
Overview of Pavement NDT Devices and Evaluation Methods
GPR Showing Large Variations in HMA Thickness (Extreme case)
24
HMA Thickness 6” 8” 2” insFWD max defl 16.2 8.04 24.3 mils
Overview of Pavement NDT Devices and Evaluation Methods
Case Studies
1) Project Segmentation2) Forensics Investigation3) Project Acceptance Testing
25
Overview of Pavement NDT Devices and Evaluation Methods
Case Study 1 Pavement Design (FWD used for project segmentation)
26
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
0.000 1.000 2.000 3.000 4.000 5.000 6.000
Defle
ctio
ns (m
ils)
Distance (miles)
SH 302 Odessa (Full investigation needed)
Section A B (Soft) C (Stiff)(Stiff)
Overview of Pavement NDT Devices and Evaluation Methods
Forensic Investigation Undertaken
Section A Section B
27
Reasonable base - good subgradeFDR or Overlay candidate
Very Poor/weak subgradeFDR to make a foundations layerBuild a new road on top
Overview of Pavement NDT Devices and Evaluation Methods 28
MODULUS 7 Mapping of Deflection Data
Section A
B
C
Pecos River Flood Plain
Overview of Pavement NDT Devices and Evaluation Methods
Case Study 2 Use of FWD in Road Failure Investigation
•Rutting/Cracking Failure during construction
•20 inches of Grade 1 Limestone base
•Underseal + 2 inches of dense graded HMA
•Both Base and HMA passed specs on density and thickness
•Is it HMA, base, subgrade or bonding problem??
Overview of Pavement NDT Devices and Evaluation Methods
Target area for detailed investigation with DCP and sampling
A BValidation locations
Overview of Pavement NDT Devices and Evaluation Methods
Conclusions and Actions
§ Focused DCP testing concluded this is a Base Compaction problem
§Density measurements did not catch problem
–One test per 3000 cu yd
–Base substantially less than OMC when test run
–“Contractor compacted base on dry side”
§HMA was good, bonding was good
§ 1 mile of project rebuilt
Overview of Pavement NDT Devices and Evaluation Methods
Case Study 3 : Use of Deflection Testing to Verify as-built structure
§ Use of Deflection devices to ensure we have built what was designed§ Proposed for Design Build Projects in
Texas§Mostly CRCP pavements with HMA
bond breaker layer and engineered base§ Test on top of HMA prior to placing
concrete§No backcalculation – target
acceptable deflections What we are trying to avoid
Overview of Pavement NDT Devices and Evaluation Methods
Summary
§ Deflection Testing is alive and well in Texas– FWD for Flexible Pavements– TPAD for Rigid§ 8 FWD’s in everyday use§ 6-8 Training schools taught per year for TxDOT designers§ Backcalculated layer Modulus values needed for Flexible Pavement Design (FPS 21)§ Merging of GPR and FWD very beneficial§ Currently pilot testing the use of the FWD to certify as designed and as constructed
are the same– Are other agencies doing this??
33
Overview of Pavement NDT Devices and Evaluation Methods
Laboratory validation of Backcalculated layer moduli values
FDR with 1% Cement 3.2% Foamed Asphalt
Take this existing section…
…and make this treated, stable base layer
Overview of Pavement NDT Devices and Evaluation Methods
SH 176 FWD Moduli at 45oF = 518 ksi
Overview of Pavement NDT Devices and Evaluation Methods
Lab Dynamic Modulus Testing of Foamed Asphalt core from SH 176
Modulus = Stress/Strain measured at different Temperatures and Frequencies
Overview of Pavement NDT Devices and Evaluation Methods
Average Backcalculated value at 45oF was close to 500 ksi
Temp oF SH 176Dynamic Modulus of
Foamed Asphalt Base1% C 3.2% FA
(ksi)
For ComparisonTypical Dynamic
Modulus of Hot Mix Asphalt
(ksi)50 496 1600
77 427 500
122 270 190
Lab Moduli values very close to Moduli from analysis of FWD deflections
Foamed asphalt behaves more like flex base than an HMA layer (which is good)
Part 2: Static and Dynamic Backcalculation
Methods and Challenges
Karim Chatti, Ph.D. F. ASCEMichigan State University
Pavement Deflections – Past, Present, and FutureTRB Standing Committee on Pavement Structural Modeling and Evaluation
Learning Objectives
• Identify different types of forward solutions• List different types of backcalculation methods• Differentiate between static and dynamic analyses• Assess when dynamic analysis may be necessary• Underline some challenges with backcalculation• Summarize advantages and limitations of methods
The Backcalculation Process
Knowns: Input and outputsUnknowns: System parameters
SystemInput Output
FeedBack
The process of estimating layer moduli by matching predicted to measured pavement deflections
System: Forward SolutionFeedback: Inverse Problem
Types of forward solutions
• from a boundary conditions point of view:• Continuum Solutions• Layered Elastic Solutions• Finite Element Analysis
2D axisymmetric or 3D FEM
• from a pavement response point of view:• Static (elastic)• Quasi-static (viscoelastic)• Dynamic (inertial)• Linear vs. non-linear stress-dependent behavior• Isotropic vs. anisotropic behavior
Types of backcalculation methods
• Optimization: • Iterative methods (Gradient-based error minimization)• Heuristic algorithms (Genetic Algorithms)
• Database: • Regression analysis• Artificial Neural Networks (ANN) solutions
• Hybrid approach:• Heuristic algorithm to seek global minimum followed by
gradient-based approach to converge to the solution
Frequency-domain Backcalculation
Pavement Model(Freq)
FWD Load(Time)
FWD Load(Freq)
Simulated Deflection
(Freq)
Simulated Deflection
(Time)
Measured Deflection
(Time)
Measured Deflection
(Freq)
FFT IFFT FFT
Time Domain Backcalculation with Frequency Domain Forward Solution
Frequency Domain Backcalculation with Frequency Domain Forward Solution
Time-domain Backcalculation
Pavement Model
Impulse Load
ContinuousLaplace/Hankel Transforms
Impulse Response in Transformed Domain
Inverse Laplace/Hankel Transforms
Impulse Response in Time Domain
FWD Load(Time)
Simulated Deflection
(Time)
Measured Deflection
(Time)
Time Domain Backcalculation
ViscoWave-II
Genetic Algorithm
Challenge with freq.-based backcalculation: FWD time records are truncated!
-2000
0
2000
4000
6000
8000
10000
12000
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
time (sec)
Load
(lb)
-1
0
1
2
3
4
5
6
7
8
9
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
time (sec)
Defle
ctio
n (m
ils)
r = 0 in. r = 8 in. r = 12 in. r = 18 in. r = 24 in. r = 36 in.r = 48 in. r = 60 in.
Load
Deflection
Iowa
-2000
0
2000
4000
6000
8000
10000
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
time (sec)
Load
(lb)
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
time (sec)
Defle
ctio
n (m
ils)
r = 0 in. r = 8 in. r = 12 in. r = 18 in. r = 24 in. r = 36 in.r = 48 in. r = 60 in.
Deflection
Load
Florida
Time- vs. Frequency-domain Backcalculation
§ Frequency-domain solutions cannot deal with truncated deflection time histories effectively:- FWD deflection pulses are truncated in time- This yields to significant errors in the frequency domain
§ Time-domain solutions can overcome tail errors in load and deflection time histories:- Can ignore inaccurate regions of FWD sensor time histories- Match only the more reliable part of the signal
Static vs. Dynamic Backcalculation:State-of-the-practice vs. State-of-the-art
Static Analysis of FWD Test
§ Peak load & deflection basin
§ Assumes load is static
X Cannot account for dynamic or viscoelastic effects
Load
Time
Sensor Offset
Peak Load
Def
lect
ion
Time
Time Lags
Def
lect
ion
Actual FWD Data Static Representation of FWD Data
No Time Information
Viscoelastic Analysis of FWD Test
üUses load & deflection time histories
üMaterial viscoelasticity incorporated
üOne step closer to reality
X Unable to simulate time lags at different sensor locations
X Unable to model free vibrationsLo
ad
Time
Actual FWD Data Viscoelastic Representation of FWD Data
Load
Time
Time
Def
lect
ion
Equal Time Lag
Def
lect
ion
Time Lags
TimeNo Free
Vibration
Dynamic Analysis of FWD Test
üUses load & deflection time histories
üViscoelasticity & inertial effects incorporated§ Time delays at different sensors§ Free vibrations
üOne more step closer to reality
üBetter potential for: § getting more accurate estimates, § backcalculating more parameters:
ü layer thicknessesü E*(𝜔𝜔) or E(t) mastercurve!
Load
Time
Actual FWD Data
Load
Def
lect
ion
Def
lect
ion
Time Lags
Time
Load
Time
Actual FWD Data
Load
Def
lect
ion
Def
lect
ion
Time Lags
Time
Dynamic representation
Free vibration Responses
-250
-50
150
350
550
750
950
1150
-150
-50
50
150
250
350
450
550
650
750
0 10 20 30 40 50 60
Def
lect
ion
(mic
rom
eter
)
Time (microseconds)
D1D2D3D4D5D6D7D8D9Stress
Stre
ss (k
Pa)
Time histories showing free vibrations
Section 16-1020 station 1
-100
100
300
500
700
900
1100
-30
20
70
120
170
220
270
320
370
0 10 20 30 40 50 60
Def
lect
ion
(mic
rom
eter
)
Time (microseconds)
D1D2D3D4D5D6D7D8D9Stress
Stre
ss (k
Pa)
Time histories showing no free vibrations
Section 16-9034 station 3There is wave propagation in both cases though!
Free vibrations = Presence of a stiff layer = Dynamics are important
No vibrations = No stiff layer = Dynamics should not be important
How prevalent is dynamic behavior?
From LTPP Database• 1224 “data points” • 17 states• 6 sections per state• 3 stations per section • 4 load levels
Two thirds of FWD tests showed dynamic behavior (free vibrations)
FWD Test on Waverly Rd. (Lansing, MI)
X When there is a stiff layer, the quasi-static solution significantly under-predicts the response of the far sensors
X Quasi-static solution cannot predict time delays of sensor deflections
X Quasi-static solution cannot predict the free vibration response
ü Dynamic solution predicts the full response due to wave propagation & viscoelasticity
Dynamic vs Static Solutions
Blue = Quasi-static (viscoelastic) solution, LAVADashed = Dynamic solution with Eac(t), ViscoWaveDotted = Measured data
Challenge in using Pavement-ME: Estimating damaged E* mastercurve
Representative frequency for EFWD :
f
Dynamic Backcalculation using Field DataUse two FWD tests data: morning & afternoon
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 1.E+08
Rela
xatio
n M
odul
us (p
si)
Reduced Time (s)Average lab Backcalculated E(t)
DYNABACK-VE
E(t) mastercurve
1.E-04
1.E-02
1.E+00
1.E+02
0 10 20 30 40 50 60Sh
ift F
acto
r
Temp (0C)
Backcalculated Drop 1 Lab-Fitting
Test temperature
range
Shift factor
Parameters Lab test/estimation Backcalculatedc1 1.510 1.58391c2 2.444 2.38488
c1+c2 3.954 3.96879c3 0.492 0.4594c4 -0.536 -0.55199a1 1.73E-04 9.159E-04a2 -1.02E-01 -1.126E-01
Ebase (psi) 20,000 20,556Esubgrade (psi) 13,500 13,759hsubgrade (in) 96 (1/r method) 98.5
Estiff (psi) - 235,241
(a) 9 a.m.
(b) 1 p.m.
(c) FWD data at 9 a.m.
(d) FWD data at 1 p.m.
0 0.01 0.02 0.03 0.04 0.0-5
0
5
10
15
20
Time (ms)
Ver
tical
Def
lect
ions
(mils
)
0 0.01 0.02 0.03 0.04 0.0
-5
0
5
10
15
20
25
Time (ms)
Ver
tical
Def
lect
ions
(mils
)
r = 0 in.r = 8 in.r = 12 in.r = 18 in.r = 24 in.r = 36 in.r = 48 inr = 60 inr = 72 in
Waverly Rd. (Lansing, MI)
(a) Sensor 1
(c) Sensor 3
0 0.01 0.02 0.03 0.04 0.05-5
0
5
10
15
20
25
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
0 0.01 0.02 0.03 0.04 0.05-5
0
5
10
15
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
(b) Sensor 6
(d) Sensor 8
0 0.01 0.02 0.03 0.04 0.05-2
-1
0
1
2
3
4
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
0 0.01 0.02 0.03 0.04 0.05-1.5
-1
-0.5
0
0.5
1
1.5
2
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
(a) Sensor 5
(c) Sensor 7
0 0.01 0.02 0.03 0.04 0.05-4
-2
0
2
4
6
8
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
0 0.01 0.02 0.03 0.04 0.05-2
-1
0
1
2
3
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
(b) Sensor 2
(d) Sensor 4
0 0.01 0.02 0.03 0.04 0.05-5
0
5
10
15
20
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
0 0.01 0.02 0.03 0.04 0.05-4
-2
0
2
4
6
8
10
Time (sec)
Def
lect
ion(
mils
)
MeasuredPredicted
Predicted deflection time histories
Dynamic Backcalculation using Field DataUse one FWD test data with good temperature gradient
Parameters Lab Backcalculatedc1 0.120 0.804351c2 4.049 3.350811
c1+c2 4.169 4.155162c3 1.112 0.905003c4 -0.423 -0.48508a1 6.66E-05 0.0011361a2 -1.41E-01 -0.13538745
Ebase (psi) - 26,183Esubgrade (psi) - 21,579hsubgrade (in) 180 (1/r method) 186
Estiff (psi) - 714,658
E(t) mastercurve
(a) Temperature profile
23.4 oC
18.4 oC
1.4”
1.4”
AC
1.4” 17.2 oC
LTPP section 350801
(a) FWD load history for Station 8
(b) Measured FWD time histories for Station 8
-10
0
10
20
30
40
50
60
0 20 40 60
Stre
ss (p
si)
Times (ms) 0 0.01 0.02 0.03 0.04 0.05 0.06-2
0
2
4
6
8
10
Time (ms)
Ver
tical
Def
lect
ions
(mils
)
r = 0 in.
r = 8 in.
r = 12 in.
r = 18 in.
r = 24 in.
r = 36 in.
r = 48 in.
r = 60 in.
DYNABACK-VE
DYNABACK-VE Results using other LTPP DataUse one FWD test data with temperature gradient
LTPP section 10101 LTPP section 6A805
LTPP section 6A806 LTPP section 300113
Hac = 7 in.oF
Db = 13.5 ft
Hac = 6 in.oF
Db = 16 ft
Hac = 7.5 in.oF
No stiff layer
Hac = 4 in.oF
Db = 14 ft
Example: FAA APT Pavement Structure
HMA surface course (P-401) and base (P-403)
DuPont Clay Subgrade CBR 5 - 7
Existing Material – CBR 20-40
P-154 Subbase course
High Strength Subgrade CBR 25-30
10”
15”
53”
30”
Pavement Model
Effect of HWD Equipment?
Backcalculated Parameters25
Advantages and Limitations
Method Static Backcalculation Dynamic Backcalculation
AdvantagesSimpleFastPast experience
More accurateCan backcalculate more parameters
HMA E(t) or E* mastercurveAccounts for stiff layer condition
LimitationsDoes not use all available informationCan be inaccurateCannot backcalculate E* mastercurve
Needs more informationComputationally expensiveLittle experience
In Summary • FWD Test is a dynamic testØStiff layer condition is important and causes amplification
of the far sensors deflections• Static BackcalculationüUses only peak load & peak deflection basinüEfficientXCan lead to erroneous results when dynamics are present
• Dynamic BackcalculationØUses the full load & deflection time historiesüAllows for backcalculating more parameters: Øe.g., E* mastercurve of the AC
XComputationally expensive
Some References
Extra slides for Q&A session
Static vs. Dynamic Forward calculation:Simple tools to diagnose stiff layer condition
Surface Modulus Using Static Solution
22 1 -0
0o
o
aE
d
2 21 o
o
aE r
r d r
Subgrade modulus Stiff layer condition or Stress-dependent material
Forward-calculation of subgrade modulus using dynamics
Physical Layer Elastic Modulus (MPa (ksi))
Poisson’s ratio
Mass density (kg/m3)
Thickness (m (in))
AC Experimental data 0.35 2300 0.1 (4) Base 150 (21.8) 0.35 2000 0.3 (12)
Subgrade 100 (14.5) 0.45 1500 Infinity
E= 112 MPa
Vr = 1/0.0077 = 130 m/s
Vr/Vs
𝑉𝑉𝑠𝑠 = 𝐺𝐺𝜌𝜌
=105 m/s
)𝐸𝐸 = 2𝐺𝐺(1 + 𝜈𝜈
Depth to stiff layer - Static analysis
a/r
Db = 100 in or 8.5 ft
a/r
No bedrock!
Depth to stiff layer - Dynamic analysis
Db 𝜶𝜶 (VsTd)
Moving Devices for Measuring Pavement DeflectionsTRB WebinarJune 24, 2020
Brian Diefenderfer, PhD, PEVirginia Transportation Research Council
Outline
• Background• Recent research
- FHWA and pooled fund studies• Beginnings of implementation
- Agency-sponsored work and pooled fund studies• Future direction
Background
• Pavement management current practices- Pavements assessed based on surface-observed condition
(percent cracked, area patched, etc.)- Affected layers is a guess- New surfacings erase performance history- Structural capacity data is rare at the network level
Background
• Structural capacity testing- FWD is the current state-of-the-practice
• Topics of concern- Safety (stationary testing)- Discrete data- Low production (miles) rate
Why Deflection Testing?
• Quantify structural condition- Structural number, k-value, load transfer, etc.- Identify areas that require rehab (i.e., bound vs unbound
layers)
• Nondestructive- Does not further damage roadway- Can be repeated over time
Why Deflection Testing?
• Recent developments (last 10 years-ish)- Apply deflection testing by vehicles that move- Goal is to move with the prevailing traffic speed
• FHWA study (2011) and SHRP2 (2013)- Identified several traffic speed deflection devices- Benefits include nearly continuous data, testing at higher
speeds (up to approx. 50 mph)- Future work to study accuracy and analysis methods
Recent Research
• FHWA study (2012-2015) and TPF-5(282)- TSDD-measured deflection versus embedded sensors- Compared qualitative ranking of structural condition with
FWD- Identified relevant analysis parameters
• Pooled Fund Study provided technology demo to 9 agencies
- Total of nearly 6,000 miles
Structural Indices
• SCI300 (SCI12)- D0-D300 (D0-D12)
• SNeff- SIP = D0-D1.5Hp- SNeff = k1*SIPk2*(Hp)k3
Nasimifar et al. (2019)- k1 = 0.4369- k2 = -0.4768- k3 = 0.8182
D0
Hp
D1.5Hp
1.5*Hp
TPF-5(282) Findings
• Short- and long-term repeatability was good- More work related to temperature correction needed
• TSDD and FWD followed similar trends- But…. not a one-to-one replacement
• Almost no relationship between deflection and surface condition
- Demonstrates need for structural testing
TPF-5(282) Main Products
• Approaches for classifying structural condition- Mechanistic approach based on tensile strain at the
bottom of the asphalt layers- Percentile from the SCI300 cumulative distribution- SNeff derived from TSDD data
• Framework to incorporate TSDD-measured structural condition within an agency PMS
- Used to enhance treatment selection
Deflection Testing by Moving Vehicles in the US
• Development- FHWA (2011) and previous efforts
• Assessment- SHRP2 (2013)
• Evaluation- FHWA (2012-2015) and other
ongoing studies• Demonstration
- TPF-5(282)
Implementation
• Agency research- At least 12 agencies are conducting their own studies
(either independently or with local universities)
• TPF-5(385), 2018-2021- Pavement Structural Evaluation Using Traffic Speed
Deflection Devices- 21 agency partners (20 states plus FHWA)
Implementation – Virginia Research
• Virginia research- Study of 4,000+ miles of TSDD testing (2017-2020)
• Objective- Study impact to PMS results- Recommend TSDD-based parameters and criteria
• History- VDOT already uses a FWD-based component in their PMS
for pavements on the interstate network
VDOT PMS Decision Process Including FWD
VTRC Report 13-R9
Implementation – Virginia Research
• Will TSDD-measured structural condition show a difference with respect to the rate of deterioration?
• Does using FWD- or TSDD-based structural data result in similar recommended rehab treatments?
• What are the cost implications of moving to TSDD data?- Additional costs, cost neutral, less expensive?
Implementation – Virginia Tested Roads
Interstate: 1,500 miles Primary: 2,500 miles
deflection
thickness
cracking
rutting and IRI
Implementation – Virginia Research Findings
• Structural condition affects performance
- Weaker sections deteriorate faster- Blue line = strongest 25%- Red line = weakest 25%
0 5 10 15
Time from last treatment (Years)
60
65
70
75
80
85
90
95
100
CCI
Implementation – Virginia Research Findings
• FWD and TSDD SNeff had similar distribution
Implementation – Virginia Research Findings
• Treatment selection changes
Increasing severity
TSDDFWD
No structural info
Implementation – Virginia Research Findings
• Cost implications- Will vary depending on the threshold for “weak” sections- Threshold can be adjusted to make costs equal
• TSDD data indices• Little practical difference between using SCI300 or SNeff to identify
structurally weak sections• SCI300 does not require the pavement thickness to calculate it and is
mechanistically related to tensile strain at the bottom of the asphalt layer
Implementation – TPF-5(385)
• Provide a means to conduct demonstration testing- How to use data to support project level decision making
in PMS- Costs (and any savings) through case studies
• Develop specs for data collection and guidelines for PMS application
• Conduct workshops and prepare training
Implementation – TPF-5(385) Current Status
• Year 1- Testing in 19 agencies on agency selected routes- Costs (and any savings) through case studies
• Year 2- 2nd round of testing underway, combination of repeating
same locations and additional routes- Identify research needs
Implementation – TPF-5(385) Research Needs
• Identified by agency partners- Guidelines and procedures to implement TSDD
measurements into PMS- Guidelines and operating conditions for data collections
Future Work Regarding Deflection Testing with Moving Vehicles• Agency research
- VDOT to continue testing additional routes• TPF-5(385)
- Develop implementation and operating guidelines- Assist with further agency implementation
• NCHRP 10-105 (Pending)- Verification of TSDDs Measurements- Objective to develop a standard practice for verification
Acknowledgements
• TPF-5(385)- Agency partners
• Virginia Tech Transportation Institute
- Gerardo Flintsch and SamerKaticha
• VDOT- Tanveer Chowdhury, Affan
Habib, Girum Merine
• ARRB- Jerry Daleiden, Nathan Kebede,
Nate Bech, Eric Botting• ARA
- Salil Gokhale• FHWA
- Nadarajah Sivaneswaran• TX DOT
- Senthil Thyagarajan
Today’s Presenters
Gonzalo Rada, Wood LLC
Tom Scullion, Texas A&M Transportation Institute
Karim Chatti, Michigan State University
Brian Diefenderfer, Virginia Department of Transportation
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