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Mostafa A. ElseifiAhmed M. Abdel-Khalek
Karthik Dasari
Implementation of
Rolling Wheel
Deflectometer (RWD) in
PMS and Pavement
Preservation
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Research Objectives
Conduct a detailed field evaluation of
the RWD system in Louisiana
Analyze collected RWD and FWD
data to assess the structural
conditions of the pavement network
Develop a methodology to implement
RWD data into existing pavement
management system
2
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Rolling Wheel Deflectometer
3
53ft
Deflection-
Measurement
SystemCooling
System
Steel-
LoadingPlates
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Field Testing Plan
Testing program was conducted overtwo phases:
Phase I: RWD testing of the complete
asphalt road network (about 1200 miles) inDistrict 5
Phase II: Detailed RWD evaluation inDistrict 5 16 test sites were tested using RWD
FWD testing conducted within 24 hrs of RWDtesting
4
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Rolling Wheel Deflectometer
Phase I Network Testing
UNION
MADISON
MOREHOUSE
OUACHITA
JACKSON
RICHLAND
LINCOLN
EAST CARROLL
WEST CARROLL
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Rolling Wheel Deflectometer
Phase II Research Sites
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SUMMARYOF FINDINGS
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Data Processing
Valid deflection measurements wereaveraged every 0.1 mile (average of
10,728 individual readings)
8
0
5
10
15
20
25
30
6 6.5 7 7.5
Logmile
Deflection,mils
528 ft
132 ft
33 ft
As averaging length decreases,
deflection variability increases
Averaging Interval
0
2
4
6
0 100 200 300 400 500 600
Interval length, ft
Standard
deviationofmeans,mils
An averaging length of 528 ft is recommended
for PMS applications to reduce random error to
approximately 1 mil
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Repeatability Analysis
2/27/2013 9
Site 6 PCI = 99
0
5
10
15
20
25
5.
01
3
5.
08
8
5.
16
3
5.
23
8
5.
31
3
5.
38
8
5.
46
3
5.
53
8
5.
61
3
5.
68
8
5.
76
3
5.
83
8
5.
91
3
5.
98
8
6.
06
3
6.
13
8
6.
21
3
6.
28
8
6.
36
3
6.
43
8
Deflection(mils)
Station (mile)
Run 1Run 2Run 3Mean
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Repeatability Analysis
2/27/2013 10
Site 11 OCI = 57
0
5
1015
20
25
30
3540
4.
913
4.
988
5.
063
5.
138
5.
213
5.
288
5.
363
5.
438
5.
513
5.
588
5.
663
5.
738
5.
813
5.
888
5.
963
6.
038
6.
113
6.
188
6.
263
6.
338
Deflec
tion(mils)
Station (mile)
Run 1 Run 2Run 3 Mean
Bridge Bridge
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Repeatability Analysis
Repeatability of
the measurements
was acceptable
with a COV
ranging from 7 to20% with an
average of 15%.
[e] Site 5 (PCI = 98) [k] Site 11 (PCI = 57)
Test Speed (mph)
Site
ID
20 30 40 50 60
AverageCOV
(%)Average Deflection
(mils)
COV
(%) COV (%)
1 16.4 16 17 14 13 ---- 15
2 17.1 14 17 18 ---- ---- 16
3 12.5 13 12 13 ---- ---- 134 15.6 6 8 9 ---- ---- 8
5 9.5 13 13 16 15 ---- 14
6 14.9 6 7 8 9 ---- 7
7 7.7 9 11 17 13 16 13
8 15.9 18 22 19 20 ---- 20
9 9.5 20 18 16 13 ---- 17
10 15.5 14 17 16 ---- ---- 16
11 19.9 15 23 ---- ---- ---- 19
12 18.4 12 26 15 ---- ---- 18
13 9.5 18 18 16 20 ---- 18
14 14.3 16 21 ---- ---- ---- 19
15 13.5 14 14 16 15 ---- 15
16 21.5 15 17 ---- ---- ---- 16
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Effect of Speed The influence of the testing speed on the measured
deflection was minimal
An ANOVA test was conducted between different speedsand revealed no statistical difference
0.00
5.00
10.00
15.00
20.00
25.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Avg.Deflection(mils)
Site ID
20 mph
30 mph40 mph
50 mph
60 mph
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Comparison Between RWD and FWD
Results
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
2.
01
2.
14
2.
26
2.
39
2.
51
2.
64
2.
76
2.
89
3.
01
3.
14
3.
26
3.
39 -
3.
09
3.
21
3.
34
3.
46
3.
59
3.
71
3.
84
3.
96
4.
09
4.
21
4.
34
4.
46 -
2.
11
2.
24
2.
36
2.
49
2.
61
2.
74
2.
86
2.
99
3.
11
3.
24
3.
36
Deflection(m
ils)
Logmile
RWD
FWD
Site 1Fair
Site 2Good
Site 3Very Good
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0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
3.
220
3.
420
3.
620
3.
820
4.
020
4.
220
4.
420
4.
620
4.
820
5.
020
5.
220
5.
420 -
9.
700
9.
900
10.
100
10.
300
10.
500
10.
700
10.
900
11.
100
11.
300
11.
500
11.
700
11.
900 -
3.
113
3.
238
3.
363
3.
488
3.
613
3.
738
3.
863
3.
988
4.
113
4.
238
4.
363
4.
488
Deflection(mils)
Logmile
RWD
FWD
Site 8
Fair
Site 9
Very Good
Site 10
Poor
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FWD vs. RWD
2/27/2013 15
Site ID Average
FWD
(mils)
Average
RWD
(mils)
Pearson
Correlation
P-value Decision
1 24.83 18.20 0.13 < 0.0001 Not Equal
2 9.58 15.79 0.65 < 0.0001 Not Equal
3 6.76 11.78 0.78 < 0.0001 Not Equal
4 7.44 15.62 0.22 < 0.0001 Not Equal
5 6.51 9.50 0.41 < 0.0001 Not Equal
6 8.97 14.99 0.66 < 0.0001 Not Equal
7 1.66 7.75 0.15 < 0.0001 Not Equal
8 10.88 15.48 0.59 < 0.0001 Not Equal
9 4.99 8.34 0.20 < 0.0001 Not Equal
10 14.58 14.01 0.44 0.19 Equal
11 26.83 19.89 0.38 < 0.0001 Not Equal12 11.58 18.41 0.44 < 0.0001 Not Equal
13 4.41 9.51 0.22 < 0.0001 Not Equal
14 8.34 14.37 0.14 < 0.0001 Not Equal
15 12.02 13.54 0.35 0.003 Not Equal
16 37.72 21.55 0.06 < 0.0001 Not Equal
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SN Prediction Approach
A regression model to predict SN from
RWD data
Develop a tool to predict pavementoverall condition (i.e., functional and
structural) based on RWD deflection
measurements and PMS data regularly
collected in Louisiana
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APPROACH 1: PREDICTIVE
MODEL FOR SN
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RWD Index (RI)
Pavement structural capacity and integrityrelates to: Deflection magnitude Avg. RWD for each
project
Variability and scattering of deflectionSDRWD
Define the RWD Index (RI): RI = average deflection (Avg. RWD) x standard
deviation (SDRWD) The RI was calculated for each site
18
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SN from RWD
Model to predict SN from RWD Data:
19
RI= RWD Index (mils2);
SD = standard deviation of RWD deflection
(mils); and
RWD = average RWD deflection (mils).
)ln(*39.1*52.23
04.19
*69.15037.6 24.081.0
SDRWD
RI
RISNeff
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Model Calibration and Validation
Model was developed and calibrated based on the research sites
Model was validated based on 52 sections with FWD and RWD
data
Use of the model at the network level is appropriate
R = 0.7469
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00
SN-RWD
SN-FWD
R = 0.7687
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00
SN-RWD
SN-FWD
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APPROACH 2: GRAPHICAL
METHOD TO PREDICT
OVERALL CONDITION OF
PAVEMENTS
21
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Analysis
Pavements were categorized into threegroups: Thin pavements 0 to 3 inches
Medium pavements 3 to 6 inches
Thick pavements greater than 6 inches
Pavement were categorized according toSN, IRI and PCI:
Good Fair
Poor
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Condition-Based Categorization
Condition
Structural Number RangePCI for all
pavements IRI for allpavementsThin Medium Thick
Poor 4 > 5 > 85 < 120
No. of
Sections38 102 84 ---- ----
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Pavement Assessment Model
Example: SN = 2.0
IRI = 180 and PCI =
75
Overall Condition:Fair
Thin pavement
If IRI > 260, Condition is Poor
If PCI < 45, Condition is PoorIf SN < 1.0, Condition is Poor
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GIS Map: SN Model
SN prediction model was applied to 220sections tested in District 5 using RWD.
25Good Fair Poor
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GIS Maps
26
PCI
SN Model
IRI
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Summary
Repeatability of RWD measurementswas acceptable - average COV at all
test speeds of 15%
RWD deflection measurements were ingeneral agreement with FWD
deflections measurements
A model was developed to estimatepavement SN based on RWD deflection
data
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Whats Next?
Extend testing to other districts
Validate and update the developed models
based on independent data:
From other states?
From another district?
Evaluate effect of speeds in summer
months
28
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QUESTIONS
