Pavement Deflections – Past, Present, and...

TRANSPORTATION RESEARCH BOARD

@NASEMTRB#TRBwebinar

Pavement Deflections –Past, Present, and Future

June 24, 2020

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#TRBwebinar

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


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


Benkelman Beam (WASHO 1952)

3

Measures Pavement Rebound

Low cost ($1800.00) widely usedSlow and labor intenseDoes not provide a deflection bowl


Automated Beam La Croix Deflectograph 1978


Curviameter Belgium Road Research Lab 1983

• Operates at around 20 mph

• Geophones embedded in chain

• Generates a deflection bowl every 5 meters,


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


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


Falling Weight Deflectometer (1982)

Weights lifted then dropped

Load PlateDeflection Sensors

8


Traditional and latest Generation FWD in Operation

9

High Speed new generation FWD1 Mile in 8 minutes at 0.1 interval


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


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


FWD Raw Data

13

Miles GPS 7 Deflections OperatorLoad Temps Comments


Stress Distribution and Deflections under FWD Loading

6/23/2020

CL

Surface

Base

Subgrade

14


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


Simple Pavement Diagnosis based on Bowl parameters

16


Hoffman and Thompson’s Area

17


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


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


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


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


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


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


Case Studies

1) Project Segmentation2) Forensics Investigation3) Project Acceptance Testing

25


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)


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


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??


Target area for detailed investigation with DCP and sampling

A BValidation locations


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


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


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


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


SH 176 FWD Moduli at 45oF = 518 ksi


Lab Dynamic Modulus Testing of Foamed Asphalt core from SH 176

Modulus = Stress/Strain measured at different Temperatures and Frequencies


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)


(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)


(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


• FWD and TSDD SNeff had similar distribution


• Treatment selection changes

Increasing severity

TSDDFWD

No structural info


• 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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Pavement Deflections – Past, Present, and...

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