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Evaluation and Validation of a Model to
Predict Pavement Structural Number Using
Rolling Wheel Deflectometer (RWD) Data
Mostafa A. Elseifi
Lloyd Guillory Distinguished Associate Professor
Department of Civil and Environmental Engineering
Louisiana State University
3504 Patrick Taylor Hall, Baton Rouge, LA 70803
e-mail: [email protected]
Kevin Gaspard
Senior Pavement Research Engineer
Louisiana Transportation Research Center
Louisiana State University
4101 Gourrier Ave., Baton Rouge, LA 70808
e-mail: [email protected]
Paul W. Wilke, P.E.
Principal Engineer
Applied Research Associates
e-mail: [email protected]
Zhongjie Zhang
Pavement Geotechnical Research Administrator
Louisiana Transportation Research Center
Louisiana State University
4101 Gourrier Ave., Baton Rouge, LA 70808
e-mail: [email protected]
Ahmed Hegab
Transportation Engineer/ Planner IV
Capital Region Planning Commission
333N 19th
Street/P.O. Box 3355,
Baton Rouge, LA 70821
Email: [email protected]
Elseifi, et al. 2
ABSTRACT 1
2
Due to the associated cost and slow test process, the use of structural capacity in 3
pavement management activities at the network level has been limited. The Rolling 4
Wheel Deflectometer (RWD) was introduced to support existing non-destructive testing 5
techniques by providing a screening tool for structurally-deficient pavements at the 6
network level. A model was developed to estimate structural number (SN) from RWD 7
data obtained in a Louisiana study. The objective of this study was to evaluate the use of 8
the Louisiana model to predict structural capacity in Pennsylvania and to compare the 9
results to other existing methods. To this end, RWD testing was conducted on 288 miles 10
of the road network in Pennsylvania in addition to FWD testing and coring on selected 11
sites. Prediction from a model to estimate SN from RWD deflection data was compared 12
statistically to the prediction obtained from FWD testing and from the Roadway 13
Management System (RMS) records used by PennDOT to calculate SN. Based on the 14
results of this analysis, the model was validated to estimate pavement SN based on RWD 15
deflection data. In general, the predicted SN was in agreement with the SN calculated 16
from the FWD. The original model with the fitted coefficients developed for Louisiana 17
showed an average prediction error of 27%. However, after the model was refitted to the 18
data set from Pennsylvania, the average error dropped to 19%. Results indicate that the 19
developed model for SN prediction from RWD provides an adequate prediction of SN for 20
conditions different than those for which it was developed in Louisiana. 21
22
23
24
25
26
Keywords: Non-Destructive Testing, Rolling Wheel Deflectometer, Structural Capacity, 27
Structural Number, RWD. 28
29
Word Count
Text 4,100
Figures 5x250
Tables 5x250
Total 6,600
30
31
32
33
34
35
36
37
38
Elseifi, et al. 3
INTRODUCTION 1
Pavement evaluation is an important step to assess the functional and structural 2
conditions of roadways for routine monitoring and in order to select proper corrective 3
actions. Functional condition is related to the roughness and ride quality of a highway 4
section. Structural condition deals with a pavement’s ability to withstand traffic loads 5
and environmental conditions, which can be measured by determining material 6
properties, layer thicknesses, and surface deflections (1). The evaluation of pavement 7
structural capacity and integrity is an important component of a Pavement Management 8
System (PMS) that can assist in the selection of suitable maintenance and rehabilitation 9
strategies (2). 10
The Falling Weight Deflectometer (FWD) is a non-destructive deflection testing 11
method that is widely used at the project-level to assess the structural conditions of in-12
service pavements. Measuring the structural capacity and integrity of existing pavements 13
using FWD and the Ground Penetrating Radar (GPR) has been adopted by many state 14
agencies as these two testing techniques can supply data about pavement structural 15
capacity, layer properties, and layer thicknesses (3). These data are necessary to support 16
efficient pavement management decisions. However, issues such as expenses involved in 17
data collection, unavailability of simplified procedures with direct results, and scarcity of 18
resources appear to represent obstacles for wide adoption of these techniques for 19
pavement testing at the network level. 20
The Rolling Wheel Deflectometer (RWD) was introduced to support existing non-21
destructive testing techniques by providing for a screening tool of structurally-deficient 22
pavements at the network level (4, 5). The use of the RWD, which measures deflections 23
at highway speeds, offers the potential to characterize the structural capacity of the road 24
network without major delays and in a cost-effective way. In 2009, the Louisiana 25
Department of Transportation and Development (LADOTD) conducted a comprehensive 26
testing program of the RWD in District 05 (6). Measurements were used to assess the 27
repeatability of RWD measurements, the effect of truck speeds, and to study the 28
relationship between RWD and FWD deflection measurements and pavement conditions. 29
Further, a model was developed for direct and simple prediction of pavement Structural 30
Number (SN) using RWD deflection data. The developed model was calibrated based on 31
RWD data collected on 16 road-sections (each 1.5 miles) in Louisiana. The model was 32
then validated based on FWD and RWD data collected on 52 road-sections in District 05 33
of Louisiana. 34
While the developed model was successfully calibrated and validated for the 35
conditions pertinent to the Louisiana road network, it is uncertain whether this model can 36
be extended to different states and for varying road conditions and designs. The 37
objective of this study was to evaluate the use of the model to predict structural capacity 38
in Pennsylvania and to compare the results to other existing methods. To this end, RWD 39
testing was conducted on 288 miles of the road network in Pennsylvania in addition to 40
FWD testing and coring on selected sites. Predictions from the model were compared 41
statistically to the predictions obtained from FWD testing and from the Roadway 42
Management System (RMS) records used by PennDOT to calculate SN (7). 43
Elseifi, et al. 4
BACKGROUND 1
1993 AASHTO Procedure for SN Determination 2
The 1993 AASHTO design guide introduced an approach for determining pavement 3
structural number using deflection data obtained from FWD testing (8). This approach 4
assumes that the subgrade resilient modulus can be obtained from a backcalculation 5
procedure by relating it to the surface deflection at a large distance from the load as 6
shown in Equation (1): 7
8
r*dP24.0M
rR = (1) 9
where, 10
MR = backcalculated subgrade-resilient modulus (psi); 11
P = applied load (psi); and 12
dr = deflection at a distance r (in) from the center of the load (in). 13
14
The effective modulus, which describes the strength of all pavement layers above the 15
subgrade, can be computed from FWD deflection measured at the center of the load plate 16
knowing the subgrade resilient modulus and the total thickness of the pavement structure. 17
These properties can be related and used to compute the effective modulus (Ep) using 18
Equation (2): 19
)M
E(
])
aD(1
1[1
)M
E*
a
D(1
11.5qa
dM
R
p
2
23
R
p
0R+
−
+
+
=
(2) 20
where, 21
Ep = effective modulus of all pavement layers above the subgrade (psi); 22
d0 = deflection measured at the center of the load plate and adjusted to a standard 23
temperature of 68oF (in); 24
q = load plate pressure (psi); 25
a = load plate radius (in); 26
D = total thickness of pavement layers above the subgrade (in); and 27
MR = subgrade-resilient modulus (psi). 28
29
Using the total thickness of pavement layers and the effective pavement modulus 30
calculated from Equation (2), the effective structural number (SNeff) can be computed 31
using the following expression: 32
33
SNeff = 0.00045 * D * (EP)1/3
(3) 34
35
where, 36
D = total thickness of the pavement layers (in); and 37
Ep = effective pavement modulus of all layers above the subgrade (psi). 38
Elseifi, et al. 5
SN Prediction from RWD Testing 1
A structural deflection index based on RWD testing is needed to assess the structural 2
integrity of pavements. In 2009, LADOTD conducted a comprehensive testing program 3
of the RWD in District 05 (6). Researchers noted that the RWD describes the 4
deterioration of the pavement structure through both an increase in the magnitude of the 5
deflection and an increase in the scattering and variability of the deflection 6
measurements. Elseifi et al. (2012) introduced a parameter known as the RWD Index 7
(RI) based on the average RWD deflection and the standard deviation for a 1.5 mile 8
segment (9): 9
10
RI = Avg. RWD deflection * Std. Dev. of RWD deflection (4) 11
12
where, 13
RI = RWD Index (mils2); 14
Avg. RWD deflection = average deflection (mils) measured on a road segment with a 15
length of 1.5 miles; and 16
Std. Dev. of RWD deflection = standard deviation (mils) of average RWD deflections 17
from those 1.5 miles pavement segments. 18
19
Based on the RI index and based on various expressions evaluated during the course of 20
the study, the following relationship between SN- and RWD-measured parameters was 21
introduced (9): 22
23
)SDln(*39.1RWD*52.23
04.19RIRI*69.15037.6SN 24.0
81.0
RWD −++
−−=−
−
(5) 24
25
where, 26
RI = RWD Index (mils2) = Avg. RWD deflection * SD of RWD deflection; 27
SD = standard deviation of RWD deflection on a road segment (mils); 28
RWD = Avg. RWD deflection measured on a road segment (mils); and 29
SNRWD = Pavement Structural Number based on RWD measurements. 30
31
Model development and calibration was based on RWD and FWD deflection data 32
collected on 16 road-segments (each 1.5 miles) in District 05 of Louisiana. Model 33
validation was based on RWD and FWD deflection data collected over the complete 34
asphalt road network (about 1,250 miles) in District 05 of Louisiana and based on RWD 35
standard testing protocol. The model accuracy was concluded to be acceptable with a 36
coefficient of determination (R2) in the validation phase of 0.77. 37
Roadway Management System 38
Due to expenses involved in data collection and the need for lane closure in order to 39
perform FWD testing, the use of the 1993 AASHTO procedure to estimate pavement 40
structural capacity on the entire road network can be challenging. To this end, the RMS 41
adopted by PennDOT incorporates a simple algorithm to predict pavement structural 42
capacity. In this approach, the pavement layer thicknesses available in the road inventory 43
are multiplied by the layer structural coefficients assumed in PennDOT’s design manual. 44
Elseifi, et al. 6
For pavements older than 9 years, reduced structural coefficients are assumed in the 1
calculation to account for pavement deterioration. The reported SNs in the RMS were 2
compared in the analysis to the predicted SN from RWD data; i.e., Equation (5). Test 3
sites considered in this study had an asphalt concrete thickness ranging from 3.5 to 12.0 4
in. and were in service for a period ranging from 5 to 16 years. Further, pavement coring 5
conducted as part of the experimental program showed good agreement with the 6
thickness report in the RMS with the exception of four sites (7). 7
EXPERIMENTAL PROGRAM 8
RWD Testing 9
The RWD consists of a 53-ft. long semitrailer applying a standard 18,000-lb. load on the 10
pavement structure by means of a regular dual-tire assembly over the rear single axle 11
(10). A general view of the 53-ft. custom designed RWD trailer is shown in Figure 1. 12
The trailer is specifically designed to be long enough to separate the deflection basin, due 13
to the 18-kip rear axle load, from the effect of the front axle load. In addition, the trailer 14
can accommodate the aluminum beam so that the laser range needed to tolerate any 15
bouncing of the trailer during operation could be minimized. 16
The latest version of the RWD, which was introduced in 2003, can collect 17
deflections at traffic speeds. Several modifications and upgrades were introduced to the 18
RWD with respect to the laser sensors, data acquisition system, and software. The laser 19
collection system was moved between the tires, and a new procedure was introduced for 20
laser calibration. The laser sensors are set to collect a reading at a fixed interval of 0.6 in. 21
at all truck speeds. In 2009, a more accurate and stable deflection measurement system 22
customized for pavement applications was installed. The upgraded system has a 4-in. 23
measurement deflection range and has an accuracy of ± 0.002 in. In the new system, four 24
Selcom Model SLS 6000 laser triangulation sensors are mounted at approximately 3.6 ft. 25
above the roadway surface with a 4-in. measurement range. The laser sensors work 26
simultaneously to determine pavement deflections under the wheel load, with one sensor 27
placed between the dual tires to determine the maximum deflection. Two additional 28
sensors are placed in front of the wheels to measure a secondary pavement deflection. 29
30 FIGURE 1 General Overview of the Rolling Wheel Deflection System 31
Elseifi, et al. 7
Applied Research Associates (ARA) was contracted by PennDOT to conduct a two-phase 1
field testing program using the RWD in Pennsylvania. The first phase covered 288 miles 2
of PennDOT road network in seven counties. In the second phase, a more detailed 3
testing program was conducted on 16 road-sections that were selected with different 4
structural configurations and surface conditions. The testing scheme consisted of 5
conducting both RWD, coring, and FWD measurements on each of the selected sites in 6
the second phase (7). FWD testing was conducted in the right wheel path at 200-ft 7
intervals. Pavement temperature was recorded in conjunction with each test. Testing 8
was conducted in April 2013. Surface deflections were corrected for variation in 9
pavement temperature by shifting the measurements to a standard temperature of 20°C 10
using the BELLS and the AASHTO 1993 methods. This method was also used to correct 11
FWD-deflection data. 12
RWD Data Processing and Filtering 13
During RWD testing, laser deflection readings are measured at 0.6 in intervals. Irrelevant 14
data such as measurements collected on top of bridge, sharp horizontal and vertical 15
curves, and at traffic signals were removed. Erroneous data may also be obtained if the 16
pavement surface is wet or in areas with severe cracking at the pavement surface. Valid 17
deflection measurements are then averaged to reduce the effects of truck bouncing and 18
vibrations on the measured deflections. The scattering of RWD data was controlled by 19
increasing the averaging interval. In addition, variability in the measurements decreases 20
rapidly with the increase in the averaging interval length until it reaches a near 21
asymptotic level. In order to minimize the effects of truck vibrations on the measured 22
deflections, individual readings were averaged every 0.1 mile. This corresponds to the 23
average of 10,728 individual readings for each 0.1 mile test interval. The averaging 24
process reduces the error in the individual measurements caused by bouncing and random 25
vibrations of the truck to within ± 1 mil of the interval mean (11, 12). 26
RESULTS AND DATA ANALYSIS 27
Comparison between FWD and RWD measurements 28
RWD measurements were compared to FWD center deflection data measured at a load 29
level of 9,000 lbs. for each test site as well as its variation with pavement conditions. 30
Figure 2 illustrates the variation of the average RWD and FWD deflections for three of 31
the test sites with contrasting pavement conditions. The average FWD and RWD 32
deflections are also reported in this figure. As shown in this figure, the scattering and 33
uniformity of the RWD data appears to follow closely the conditions of the roadway. For 34
example, uniform and small deflections were measured for Site 2, which was in good 35
condition. In contrast, high and scattered deflections were measured for Sites 1 and 3, 36
which were in poor and fair conditions, respectively. As shown in Figure 2, the trends in 37
the average RWD deflection were also in agreement with the average FWD deflections 38
for the different sites. From these results, it is observed that both the mean and variation 39
of the deflections are useful measurements obtained from RWD testing. 40
41
Elseifi, et al. 8
1 FIGURE 2 Variation of RWD Deflection for Three Typical Sections 2
3 An Analysis of Variance (ANOVA) with a significance level of α = 0.05 was used to 4
determine the statistical differences between FWD and RWD deflections. Within 5
ANOVA, individual pairwise comparisons (i.e., FWD vs. RWD) were conducted using 6
Tukey’s Comparison Test. In this test, each pair of means is compared statistically using 7
the following equation: 8
9
�� ������
� (6) 10
11
where, 12
YA = larger of the means being compared; 13
YB = smaller of the means being compared; 14
SE = standard error of the data. 15
16
The qs calculated from Equation (6) was compared to the qcritical obtained from a 17
studentized range distribution. If the qs value is greater than the qcritical, the two means are 18
found to be significantly different. Results presented in Table 1 show that there was no 19
statistical difference between FWD and RWD deflections measured in the 16 test sites 20
(groupings with the same letter are statistically equivalent). This was anticipated as both 21
systems are based on the same concept that thin, distressed, and soft pavements exhibit 22
greater deflections than thick and stiff pavements in good conditions. These observations 23
support the validity of RWD measurements as compared to FWD measurements at the 24
Elseifi, et al. 9
network level. Nevertheless, it is important to note that while both test methods report 1
similar trends in deflection measurements, the applications of each test method remain 2
different. While RWD is recommended as a screening tool at the network level to 3
identify structurally-deficient sections, the FWD may be applied as a more accurate 4
structural evaluation tool, by assessing the structural capacity of the pavement and by 5
conducting a complete backcalculation of layer moduli. 6
7
Table 1 Statistical Comparison between FWD and RWD Center Deflection 8 9
Average (µm) STDEV. (µm) Tukey Grouping
15.7 (FWD) 8.6 A
18.9 (RWD) 6.4 A
10
Model Validation 11
Equations (4) and (5) were applied to the RWD deflection data collected in the two-phase 12
field testing program in Pennsylvania in order to predict in-service pavement structural 13
number. As previously noted, 16 road-sections were tested using FWD and RWD and 14
288 miles of PennDOT road network were solely tested using the RWD. To validate the 15
model, prediction of the model was compared statistically to the prediction obtained from 16
FWD testing and from the Roadway Management System (RMS) records used by 17
PennDOT to calculate SN. Figure 3 shows the comparison between the FWD-calculated 18
SN and the RWD-calculated SN for the 16 sites based on Equation (5). In general, the 19
predicted SN from RWD was in agreement with the SN calculated from FWD. The 20
original model with the fitted coefficients developed for Louisiana (SN RWD Model) 21
showed an average prediction error of 27%. However, after the model was refitted to the 22
data set from Pennsylvania, the average error dropped to 19%. The prediction error was 23
calculated in reference to the SN calculated from FWD (Error [%] = (SNRWD – SNFWD) x 24
100/ SNFWD). It is also observed that the ranking obtained for the 16 road-sections was 25
similar from both FWD and RWD. Table 2 reports the fitted coefficients obtained from 26
the Louisiana and Pennsylvania data sets. 27
28
Elseifi, et al. 10
1 FIGURE 3 Comparison of the SN Predicted from Different Methods 2
3
4
Table 2 Fitted Regression Coefficients for the Model in Equation (5) 5 6
Fitted Data Set A1^
A2 A3 A4 A5 A6 A7
Pennsylvania -5.89 -163.59 -0.72 17.75 18.54 -0.20 1.07
Louisiana -6.37 -150.69 -0.81 19.04 23.52 -0.24 1.39
^ ln(SD)*ARWD*A
ARI
RI*AASN
7
A
5
4
A
2
1RWD
63
−++
+= 7
8
Individual pairwise comparisons (i.e. SN FWD vs. SN RWD LA) were conducted using 9
Tukey’s Comparison Test at a 95% confidence interval, Table 3. The reported SNs in the 10
RMS database are also presented in Table 3 and were compared to the SNs predicted 11
from RWD and FWD. Results presented in Table 3 shows that there was no statistical 12
difference between the structural numbers obtained from FWD and RWD in the 16 test 13
sites; groupings with the same letter are statistically equivalent and a two-letter grouping 14
means that the data set can be statistically classified in either group. These results 15
indicate that the developed model for SN prediction from RWD is adequate based on its 16
prediction of SN for conditions different than the ones it was originally developed for. 17
18
19
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Pavem
ent
Str
uct
ura
l N
um
ber
(S
N)
Site ID
SN FWD
SN RWD LA
SN RWD PA
Elseifi, et al. 11
Table 3 Statistical Comparison between Structural Numbers Obtained from 1
Different Methods 2 3
Prediction Method Average SN Tukey grouping
SN RWD LA 3.8 A
SN FWD 3.4 A/B
SN RMS 2.8 B
4
The developed model was also applied to 288 miles of in-service pavements that were 5
tested in Pennsylvania using RWD as part of the first phase of the testing program. 6
These pavements, which are present in seven counties, cover a wide array of pavements 7
with various ages, structural configurations, subgrade properties, and traffic loads. RWD 8
testing was conducted on the selected sections and deflection values were obtained for 9
each pavement segment. Average RWD deflection, Standard Deviation, and RWD index 10
were calculated for each section and were substituted into Equation (5) to estimate the SN 11
values of the pavements. Figure 4 compares the SN predicted from RWD to the SN 12
reported in the RMS based on estimation of layer thicknesses and structural coefficients. 13
While it appears that both methods follow similar trends, the coefficient of determination 14
(R2) was only 0.54. Further, Tukey’s Comparison Test at a 95% confidence interval 15
showed that both methods are statistically different, Table 4. 16
17
18
Table 4 Statistical Comparison between Structural Numbers Obtained from 19
RWD and RMS 20 21
Prediction Method Number of Data
Points
Average SN Tukey grouping
SN RWD 348 4.649 A
SN RMS 348 4.022 B
22
Elseifi, et al. 12
1 FIGURE 4 Comparison of the SN Predicted from RWD and RMS 2
3
APPLICATIONS OF THE STRUCTURAL MODEL AT THE NETWORK LEVEL 4
The structural capacity prediction model was applied to the 288 miles tested in 5
Pennsylvania using RWD. For each section, the average RWD deflection and the RWD 6
Index were calculated. The structural number was then calculated based on Equation (5), 7
and the results were incorporated into a GIS map, Figure 5. Threshold values were set 8
for the SN values to define good, fair, and poor pavement conditions based on typical 9
PMS thresholds adopted by the authors in previous research, Table 5 (6). Pavement sites 10
were grouped into three categories for analysis: 11
12
• Thin pavements – less than 3 in. of AC 13
• Medium pavements – 3 to 6 in. of AC 14
• Thick pavements – more than 6 in. of AC 15
16
GIS maps may be used to identify homogeneous sections, distressed pavements, as well 17
as to display the response of the RWD to different pavement conditions. Based on the 18
results presented in Figure 5, PennDOT may identify the sites in poor structural 19
conditions. In addition, a district may elect either to conduct additional testing using 20
FWD on sites in poor structural conditions, or to select a rehabilitation treatment option 21
that is suitable for structurally-deficient pavements. 22
23
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
SN
RW
D
SN RMS
Elseifi, et al. 13
1 FIGURE 5 Applications of the Structural Capacity Model to Identify 2
Structurally-Deficient Pavements 3 4
Table 5. Threshold Values for SN for Different Pavement Conditions 5 6
Pavement
Condition
Structural Number Range
Thin1
Medium2 Thick
3
Poor < 2 < 3 < 4
Fair 2 to 3 3 to 5 4 to 7
Good > 3 > 5 > 7
1: AC thickness < 3 in.;
2 AC thickness: 3 to 6 in.; 7
3 AC thickness > 6 in. 8
9
SUMMARY AND CONCLUSIONS 10
The objective of this study was to evaluate the use of a regression-based model to predict 11
structural capacity in Pennsylvania and to compare the results to other existing methods. 12
To this end, RWD testing was conducted on 288 miles of the road network in 13
Pennsylvania in addition to FWD testing and coring on selected sites. Prediction of the 14
model was compared statistically to the prediction obtained from FWD testing and from 15
the Roadway Management System (RMS) records used by PennDOT to calculate SN. 16
Based on the results of this analysis, the following conclusions are drawn: 17
Elseifi, et al. 14
• The scattering and uniformity of the RWD data appears to follow closely the 1
conditions of the roadway. This test method appears to properly reflect pavement 2
conditions and structural integrity of the road network by providing for a greater 3
average deflection and scattering for sites in poor conditions. 4
• A model was validated to estimate pavement SN based on RWD deflection data. In 5
general, the predicted SN was in agreement with the SN calculated from FWD. The 6
original model with the fitted coefficients developed for Louisiana showed an average 7
prediction error of 27%. However, after the model was refitted to the data set from 8
Pennsylvania, the average error dropped to 19%. 9
• Results indicate that the developed model for SN prediction from RWD is adequate 10
based on its prediction of SN for conditions different than the ones it was originally 11
developed for. Although the SN expression developed is independent of the 12
pavement thickness and layer properties, it provides promising results as an indicator 13
of structural integrity of pavement structure at the network level. 14
15
Based on the results of this study, further validation and evaluation of the proposed 16
model is recommended in Louisiana and by other state agencies. 17
ACKNOWLEDGEMENTS 18
The authors acknowledge the Pennsylvania Department of Transportation (PennDOT) for 19
providing the data set used in this study. The contents of this paper do not necessarily 20
reflect the official views or policies of the Pennsylvania Department of Transportation. 21
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