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Use of FWD data analysis for mechanisticbased pavement analysis and design

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Use of FWD data analysis for mechanistic-based pavement analysis and design ... Wood Floor Joist. Example. P. d. L. P. d. L (Schwartz, 2001) Empirical 'Rule of 2' ... – PowerPoint PPT presentation

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Title: Use of FWD data analysis for mechanisticbased pavement analysis and design


1
Use of FWD data analysis for mechanistic-based
pavement analysis and design
16th Annual Falli8ng Weight Deflectometer Users
Group Conference Des Moines, Iowa - October 1-2,
2007
  • Kasthurirangan Gopalakrishnan,
  • Research Scientist
  • and
  • Halil Ceylan, Assistant Professor
  • Civil, Construction and Environmental Engineering

2
Acknowledgements
  • The support provided by the Iowa DOT for the
    Nondestructive Evaluation of Iowa Pavements
    research project is greatly appreciated.

3
Pavement Design
  • Where are we currently?

State-of-Practice
State-of-the-Art
Mechanistic- Empirical
Empirical
Mechanistic
(Schwartz, 2001)
4
Empirical vs. Mechanistic-Based Design
Wood Floor Joist Example
P
P
d
d
L
L
(Schwartz, 2001)
5
Empirical Design
  • Based on results of experiments or experience
  • Requires many observations to establish links
    between design variables and performance
  • Not necessary to establish scientific basis of
    observed relationships
  • Examples AASHTO design methodology, CBR
    thickness design, R-value based design

6
Mechanistic-Based Design
  • Combines both mechanistic and empirical aspects
  • Mechanistic component involves determining
    pavement responses due to loading through
    mathematical models
  • Empirical component relates the pavement
    responses to pavement performance
  • Each key distress type is associated with a
    critical pavement response

7
Inputs Structure Materials Traffic
Climate Both mean and standard deviations inputs
are required
Selection of Trial Design
Structural Responses (s, e, d)
Performance Prediction Distresses Smoothness
Revise trial design
Design Reliability
Design Requirements Satisfied?
Performance Verification Failure criteria
No
Yes
Mechanistic-based Design Framework and Components
Final Design
8
M-E Pavement Design Guide (MEPDG)
General Information
Inputs Traffic Climate Structure
View Results and Outputs
Status and Summary
9
How FWD data can best be used with the MEPDG?
  • FWD based materials characterization
  • Backcalculated pavement layer moduli
  • Environmental effects model
  • Distress models
  • Primary response models
  • Stress-dependent characteristics

10
Structural Evaluation of Existing Pavements
  • Obtain and compile historical data
  • First (windshield) survey
  • Preliminary data evaluation
  • Second (detailed) survey
  • Visual distress survey
  • Nondestructive testing
  • Coring, sampling, and destructive testing
  • Laboratory testing

11
Nondestructive Testing - FWD
  • FWD tests have the following advantages
  • Simulate a moving wheel load
  • Measure pavement response-deflection basin
  • Require no fixed reference
  • Relatively fast

12
Falling Weight Deflectometer
13
FWD Sensor Configuration
12 in.
12 in.
12 in.
12 in.
12 in.
12 in.
12 in.
Loading Wheel Contact Area
Sensor
14
FWD Sensor Offsets
Sensor Offsets (in)
Sensor Arrangement
-12
0
8
12
18
24
36
48
60
72
?
?
?
?
?
?
?
Uniform Spacing
?
?
?
?
?
?
?
SHRP Specification
Iowa DOT FWD machine
?
?
?
?
?
?
?
?
?
15
Example of a FWD Deflection Basin
P
Load plate
d36
d12
d0
d60
AREA
16
FWD-Deflection Testing
  • Typical uses of FWD deflection data
  • Determine areas with excessive deflections
  • Determine variability along project
  • Backcalculate pavement material properties
  • To compute LTE for JCP
  • Void detection under PCC slabs

17
Backcalculation Software
  • Flexible pavements
  • BOUSDEF, EVERCALC, MODULUS, MODCOMP, and others
  • Rigid pavements
  • Best fit spreadsheet, AREA spreadsheet, and others

18
Advanced Approaches to FWD Data Analysis
  • ANN based models
  • Analyze complex and nonlinear systems
  • Nonlinear geomaterials characterization
  • State-of-the-art FE-based structural model
  • Real-time analysis
  • Analyze 100,000 deflection basins in less than 1
    second!
  • Perfect tools for both project specific and
    network level FWD testing

19
ANN Methodology
INPUTS Pavement Layer Properties(E, h)
Knowledge Database(E, h, Deflections, s, e)
Structural Model Cri. Pavement
Responses(Deflections, s, e)
Neural Network Training
Non-destructive testing (Deflections, h)
Layer Properties Predictions(E, s, e)
Neural Network Models
20
Pavement Systems
  • Flexible pavements (ILLI-PAVE database)
  • Conventional Flexible Pavements (CFP)
  • Full Depth Pavements (FD)
  • Rigid pavements (ISLAB database)
  • Composite pavements (DIPLOMAT database)

21
ANN Based Models
D0
D8
D12
D18
D24
D36
D48
D60
4-Deflection ModelsD0,D12,D24,D36 6-Deflection
ModelsD0,D12,D24,D36,D48,D60 7-Deflection
ModelsD0,D8,D12,D18,D24,D36,D60 8-Deflection
ModelsD0,D8,D12,D18,D24,D36,D48,D60
22
Training Set Parameters
23
ANN Backcalculation Models
Meier and Rix (1994)
24
ANN Based Models
  • Backpropagation ANNs

Output Vector
Target Vector
Input Vector
Najjar (2000)
25
ANN Performance Prediction of EAC
7
ANN Model CFP-E
-(4)-(5-21 kips)
AC
Millions
Testing Set 1,500
6
AAE () 1.03
RMSE 37 ksi
5
(psi)
2
R
0.99
AC
4
3
ANN Predicted E
2
1
Line of Equality
0
0
1
2
3
4
5
6
7
Millions
Given E
AC
26
ANN Performance Prediction of EPCC
(x103 psi)
PCC
E
ANN Predictions for
27
ANN Performance Prediction of ks
ANN Model 7 Deflection-k
s
1000
Testing Set 1,500
AAE () 0.26
RMSE 2 psi/in
800
(psi/in)
2
R
1.0
s
600
400
ANN Predicted k
200
Inputs D
, D
, D
, D
, D
,
0
8
12
18
24
D
,
D
, h
, h
36
60
AC
PCC
Output k
Line of Equality
s
0
0
200
400
600
800
1,000
Given k
(psi/in)
s
28
Evaluation of ANN Models Using Field Data
29
EAC Prediction Performance
Iowa-(5-3-2005)Clarke County I-35(CFP - 4DEFL
- 5-21kips)
Millions
6
5
FILTERED DATA
4
EAC (psi)
3
2
1
0
33
34
35
36
37
38
39
Conventional Flexible Pavement
Mileage
30
ERi Prediction Performance
16
Carlyle - Staley - Windsor (IL)
Thousands
12
8
ERi (psi) ANN Prediction
4
Carlyle
Staley
Windsor
0
0
4
8
12
16
Thousands
Full Depth Asphalt Pavements
ERi (psi) Statistical Algorithms
31
EPCC Prediction Performance
Rigid Pavements
National Airport Pavement Test Facility FWD
Data
32
FWD Data Analysis Software
33
FWD Data Analysis Software
34
FWD Data Analysis Software
35
Conclusions
  • ANNs can learn complex multi-dimensional mappings
  • ANN based models as surrogates for response
    analysis models in MEPDG
  • Backcalculation of stress dependent layer
    properties

36
Conclusions
  • Do not require seed moduli
  • Accuracy of ANN depends on quality of FWD data
  • FWD-GPR integration

37
THANK YOU!
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