TECHBRIEF Pavement Structural Evaluation

at the Network Level

FHWA Publication No.: FHWA-HRT-15-075

FHWA Contact: Nadarajah Sivaneswaran, HRDI-20, (202) 493-

3147, nadarajah.sivaneswaran@dot.gov.

This document is a technical summary of the Federal Highway 

Administration (FHWA) report, Pavement Structural Evaluation at the Network Level (FHWA-HRT-15-074).(1) It addresses 

the use of traffic speed deflection devices for the structural 

evaluation of pavements at the network level. Highlights from 

three major efforts—gathering information to identify viable

devices, performing field trials at or near the Minnesota 

Department of Transportation MnROAD pavement test track 

facility near Maplewood, MN, to evaluate viable devices, 

and analyzing data to identify and select the best deflection 

indices—are presented. Suggestions for implementation of the 

results within network-level pavement management system 

(PMS) applications are also provided as well as recommended 

technology improvements.

Introduction

State highway agencies (SHAs) invest billions of dollars each 

year on providing and managing the transportation infrastructure, and pavements represent a major component of those 

assets. Prudent and farsighted pavement investment decisionmaking is one of the most critical, costly, and complex 

elements. At the heart of the decisionmaking process is the 

PMS, which provides condition indices or scores for each 

pavement segment in the network. Current PMSs are driven 

by distress (i.e., cracking, rutting, faulting, etc.) and ride 

quality, typically in the form of the International Roughness 

Index (IRI) as key pavement performance indicators. While 

both merit emphasis within the PMS process, another important indicator to making rational pavement investment 

decisions is structural adequacy, which is most often 

determined based on deflection testing. 

There is presently a large array of deflection testing 

equipment, but the most commonly used device in the 

United States since the 1980s is the falling weight deflectometer (FWD). While FWDs provide many benefits, they are 

not without shortcomings. Because they are a stop-and-go 

operation, lane closures are required, which cause traffic 

disruptions. Additionally, production rates are lower than those 

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

 www.fhwa.dot.gov/research
2

associated with a continuous testing operation. 

These shortcomings are especially important in 

terms of network-level PMS applications, which 

typically require information on large pavement 

networks measuring in the thousands of miles.

To overcome these shortcomings, organizations 

in the United States and Europe have developed 

devices that can measure pavement deflections at posted traffic speeds. Recognizing the 

potential benefits of these devices, FHWA 

commissioned a research study to assess and 

evaluate the capabilities of existing devices 

and develop methodologies for enabling their 

use for pavement structural evaluation at the 

network level. The ultimate goal was to establish a reliable measure of pavement structural 

condition based on traffic speed deflection 

technology. This TechBrief presents the major 

findings, conclusions, and recommendations 

with a focus on network-level PMS applications.

Viable Devices and Field Trials

Much of the initial effort focused on gathering 

information related to potentially viable devices. 

A literature review was performed to investigate 

and evaluate previous and ongoing research 

projects related to available traffic speed deflection devices (TSDDs) that had the potential to 

meet the project objectives. It was concluded 

based on the literature reviewed that both the 

Applied Research Associates, Inc. (ARA) Rolling 

Wheel Deflectometer (RWD) and the Greenwood 

Engineering Traffic Speed Deflectometer (TSD) 

were potentially viable TSDDs that merited 

further evaluation.(1) Photographs of the two 

devices are shown in figure 1 and figure 2, 

respectively.

To augment the literature review findings, 

questionnaires were developed and sent to the 

device manufacturers as well as owners 

and users of the devices. Table 1 provides a 

summary of the two devices based on information provided by the manufacturers. Updates 

were made to both devices subsequent to the 

questionnaires (e.g., number of measurement 

locations for the RWD and TSD devices evaluated in this study were two and six, respectively).

Interviews were also conducted to follow up 

with specific questions or to pursue clarification. 

These activities further reinforced the RWD and 

TSD as TSDDs potentially capable of meeting 

the project objectives.

In light of the findings from the literature review 

and user and owner questionnaires and interviews, a work plan was developed to conduct field trials to confirm that the two TSDDs 

selected met a minimum set of specifications 

related to the structural evaluation of pavements at the network level. The most critical 

specifications were accuracy and precision of 

the measurements, but other specifications also 

included monitoring of the applied load, operating speed, distance between measurements, 

and reporting of measured deflections. 

The MnROAD facility was selected as the primary site for the field trials since it provided 

a multitude of test sections in one location as 

well as a wealth of readily available information, including pavement structure, pavement 

condition, and environmental and dynamic load 

response data. In addition, an 18-mi (29-km) 

roadway network loop in Wright County, MN, 

was tested. This loop provided not only realistic 

Figure 1. ARA RWD.

Figure 2. Greenwood TSD.
3

test sections, but it also contained tight turns 

and rolling hills that provided data to evaluate 

the effects of horizontal and vertical curves.

In addition to the existing MnROAD sensors (i.e., 

linear variable differential transformers, strain 

gauges, pressure cells, moisture gauges, thermocouples, and tipping buckets), 4 geophones 

and 1 accelerometer were installed to measure 

deflection velocity and displacement parameters at 4 cells for a total of 20 sensors. The 

pavement cross sections for these cells are 

shown in figure 3, which include three flexible cells covering a range of stiffnesses and 

one rigid pavement cell. The sensors were 

embedded in the right wheel path at each 

Table 1. Questionnaire summary sheet—device comparison.

Parameter/Device RWD TSD

Measurement type Deflection Deflection velocity

Measurement location of interest Behind centerline of load axle 

(at 7.25 inches)

Ahead of centerline of load axle 

(three locations)

Operation speed 5–60 mi/h 20–55 mi/h

Sampling frequency 0.6 inch 0.8 inch

Accuracy 2.5 mil 4 mil/s

Applied dual-tire load 9.5 kip 11.5 kip

Number of measurements One (device used in field 

study had been upgraded to 

two measurements)

Three (device used in field 

study had been upgraded to 

six measurements)

Capability of computing Surface 

Curvature Index (SCI)

Yes (when second sensor is 

added)

Yes (interpolation needed)

Complete deflection bowl No Yes (interpolation needed)

1 inch = 25.4 mm

1 mi/h = 1.61 km/h

1 mil = 0.0254 mm

1 kip = 4.45 kN 

Cell 34 Cell 19 Cell 3 Cell 72

Clay

Clay

Clay

Clay

4 inches of asphalt 

concrete (AC) 5 inches of AC

3 inches of AC

9 inches of portland

cement concrete (PCC) 6 inches of full-depth 

reclaimed asphalt (FDR)

with engineered emulsion

12 inches of class 6 12 inches of class 5 2 inches of FDR

8 inches of class 7 2 inches of class 5

33 inches of class 3

12 inches of class 3

7 inches of granular

Figure 3. Pavement structure cross section of accuracy cells.

1 inch = 25.4 mm
4

cell—two of the geophones and the accelerometer were installed along the center of the 

wheelpath, while the other two geophones had 

a 6-inch (152.4-mm) offset to either side of the 

wheel path center. The performance of each 

sensor was then verified using an FWD. One of 

the FWD sensors was placed directly on top of 

one of the embedded sensors, and the deflections reported by the FWD were then compared 

with the corresponding deflections reported by 

the embedded geophones and accelerometers. 

The results from this activity from all 20 installed 

sensors are presented in figure 4. The deflections from the two systems were similar.

The installation of the sensors took place 

the week of September 16, 2013, and the actual 

field trials took place during the week of 

September 23, 2013. Data from the sensors were 

used to establish the accuracy of the TSDDs. 

Specifically, accuracy was established by statistically comparing the results measured with 

the newly installed sensors with those reported 

by the TSDDs on three separate repeat passes. 

The deflection measurements from the rigid cell 

were not used because the slab did not show a 

localized deflection basin, and the magnitudes 

of the signals reported with the TSDDs and 

newly installed sensors were too small to be 

accurate given their stated sensitivity. Figure 5 

and figure 6 show the overall accuracy results 

for the three flexible cells for the TSD and RWD, 

respectively. The constant, the slope of the bestfit line through the data, average deviation, R2

, 

and SEE were used to assess accuracy.

y = 1.01x

R2 = 0.99

SEE = 0.47 mil

0

5

10

15

20

25

0 5 10 15 20 25

FWD Deflection (mil)

R Geophone Deflection (mil) 2 = Coefficient of determination.

SEE = Standard error of estimate.

1 mil = 0.0254 mm

Figure 4. Evaluation of performance of embedded sensors with FWD.

y = 1.12x

R2 = 0.94

SEE = 80.38 mil/s

Avg deviation = 12 percent

0

200

400

600

800

1000

1200

1400

1600

0 200 400 600 800 1000 1200 1400 1600

TSD Deflection Velocity 

(mil/s)

Geophone Deflection Velocity (mil/s)

Cell 03

Cell 19

Cell 34

Confidence Interval

Prediction Limit

Figure 5. Comparison of geophone and TSD measurements.

1 mil = 0.0254 mm
5

The precision analysis included almost all cells 

at the MnROAD facility plus the 18-mi (29-km) 

loop in Wright County, MN. At the MnROAD 

facility, the TSDDs made up to five passes at two 

to three different speeds two times a day (early 

morning and late afternoon). The results from 

different passes for each device were plotted 

and visually inspected to confirm the proper 

alignment of the data (see figure 7). Statistical 

analyses were then carried out between each 

two individual pairs of data collected in different passes. Figure 8 illustrates the comparison 

of data from the first and second passes of the 

TSDDs. Statistical parameters such as the R2 

value, slope of best-fit line, and SEE were estimated for each pair. In addition, the minimum, 

maximum, and median values of each of these 

statistical parameters were extracted, and histogram plots were developed to visually evaluate 

the distributions of the data.

While the performance of the RWD and TSD 

varied under different conditions, it was found 

that both devices were capable of providing

reasonably accurate and precise pavement

response measurements. Moreover, the findings from the accuracy and precision analyses 

were used to recommend optimum operational 

conditions and device limitations.

Deflection Index-Pavement 

Response Relations

Having established that the TSDDs in question 

were acceptable, the 3D-Move software, which 

estimates dynamic responses at any given 

y = 1.11x

R2 = 0.86

SEE = 2.2 mil

Avg deviation = 11 percent

0

5

10

15

20

25

30

0 5 10 15 20 25 30

RWD Deflection (mil)

Geophone Deflection (mil)

Cell 03

Cell 19

Cell 34

Confidence Interval

Prediction Limit

1 mil = 0.0254 mm

Figure 6. Comparison of geophone and RWD measurements.

0

10

20

30

40

50

60

0 2000 4000 6000 8000 10000 12000 14000

Deflection Slope (mil/ft)

Distance (ft)

Comparison of Various Passes

Pass 1

Pass 2

Pass 3

Pass 4

1 mil/ft = 0.083 mm/m

1 ft = 0.305 m

Figure 7. Precision comparison of passes.
6

point within the pavement structure using a 

continuum-based finite layer approach, was 

calibrated for use in the development of a 

methodology for incorporating TSDD measurements into network-level PMS applications.(2) A 

key element in the calibration was simulating 

pavement deflections using numerical models 

with a focus on understanding the parameters 

that affect the TSDD measurements, including 

vehicle speed, loadings, and pavement layer 

properties. 

Numerous calibration runs were performed, and 

the results ultimately bracketed the measured 

deflection time histories (peak and basin) from 

the project geophones. As shown in figure 9, 

there was a good comparison (R2 = 0.94) between 

computed (by 3D-Move) and measured (by sensors) maximum deflections. In addition, it was 

also important to capture the characteristic of the 

deflection basins (shapes and pulse width) since 

the TSDDs use measured deflections at various 

radial distances to estimate pavement structural 

conditions. As was the case with the maximum 

deflections, the calibrated 3D-Move software 

was able to bracket the deflection basins.

The 3D-Move software was further calibrated 

using strain measurements taken by MnROAD 

strain gauges at various interior pavement locations. Since load-induced strains are critical 

inputs to performance predictions, this effort 

was essential in the applicability of the 3D-Move 

software for pavement response predictions

to be used in identifying the most promising

indices from TSDD measurements that best 

relate to pavement structure. Again, numerous 3D-Move runs were performed, and the 

results were compared to the MnROAD 

sensor measurements, which showed that the 

maximum strains correlated well between the 

two data sources.

Using the calibrated 3D-Move software, an 

analytical investigation was then performed to 

explore relationships between load-induced 

pavement structural-related response and 

the corresponding surface deflection basinrelated indices. Pavement structural capacity 

could be estimated from performance prediction equations, which relate load-induced 

pavement responses to one or both of the 

y = 0.9514x

R2 = 0.8412

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Pass 2 Deflection Slope (mil/ft)

Pass 1 Deflection Slope (mil/ft)

Comparison of Deflections from Pass 1 and Pass 2

Figure 8. Precision linear comparison of passes.

1 mil/ft = 0.083 mm/m

Figure 9. 3D-Move computed versus measured 

maximum deflections.

1 mil = 0.0254 mm
7

following two pavement distresses: AC fatigue cracking and subgrade rutting. The critical 

load-induced pavement responses that relate 

to these two distresses were the maximum tensile strain at the bottom of the AC layer and the 

vertical compressive strain on top of the subgrade, respectively. In light of the project goal 

and objectives, the focus of this project was 

on fatigue cracking and, therefore, AC tensile 

strains.

Numerous deflection basin-related indices have 

been proposed by researchers that are perceived 

as strong predictors of the critical structuralrelated responses and conditions (or capacity) 

of the pavements.(1) Many of these, as well as 

others proposed by the project team, were 

investigated for a total of 75 indices. Each 

index was evaluated as follows:

1. The deflection indices that showed good 

correlation with the critical pavement 

responses were identified using the 3D-Move 

calibration results (42 datasets), which were 

based on the TSDD field trials carried out 

at the MnROAD facility. 

2. A sensitivity analysis of the correlations 

associated with various deflection indices

(not limited to those from step 1) was 

performed using a set of 36 pavement 

structures with different layer thicknesses 

and moduli at several vehicle speeds as 

input to 3D-Move.

3. The robustness of the correlations identified 

in the first two steps was further explored 

by considering a much larger database of 

approximately 15,000 pavement structures 

generated using the layered elastic program, Jacob Uzan Layered Elastic Analysis 

(JULEA).(3)

After evaluating the indices based on the first 

two datasets, the most promising indices were 

identified and are summarized in table 2, which 

includes the name of each index as well as 

its symbol and R2 value. The subscript in the 

symbol identifies the radial distance in inches 

(from the load center) at which the corresponding deflection values are used to compute the 

index. When two subscripts are included and 

separated by a minus sign, it indicates that the 

index is the difference in deflection values at the 

radial distances denoted by the subscripts. For 

those indices where only one value is provided, 

the index makes use of the deflections value 

corresponding to that radial distance as well as 

the deflection at a radial distance of zero or, if 

it includes the letter “m,” at the radial distance 

where the maximum deflection occurs, which 

is not necessarily zero. The only exception is the 

Area Under the Pavement Profile (AUPP) Index, 

which is based on the deflections measured at 

0, 12, 24, and 26 inches (0, 304.8, 609.6, and 

660.4 mm) from the load center. The equations 

for each index are provided in the corresponding report.(1)

Table 2. Summary of promising deflection indices.

Best Indices (With Respect to 

Maximum Horizontal Strain) Symbol R2

Radius of Curvature (Horak 1)

R112 0.94

R118 0.97

R124 0.95

Radius of Curvature (Horak 2)

R212 0.92

R218 0.96

R224 0.97

R236 0.94

R248 0.90

SCI

SCI12 0.94

SCI18 0.97

SCI24 0.95

SCIm8 0.92

SCIm12 0.96

SCIm18 0.97

SCIm24 0.94

Deflection Slope Index (DSI)

DSI4 – 8 0.94

DSI4 – 12 0.97

DSI4 – 18 0.95

DSI4 – 24 0.92

DSI8 – 12 0.96

DSI8 – 18 0.91

Tangent Slope (TS) TS8 0.97

Slope of Deflection (SD)

SD12 0.94

SD18 0.97

SD24 0.95

AUPP AUPP 0.95
8

Of the indices listed in table 2, Horak’s radius 

of curvature equation (R212) seemed to be the 

overall best index for two reasons: (1) its R2

was greater than 0.9 and (2) it was minimally 

affected by the AC thickness. The SCIm8 Index is 

also a promising index regardless of AC thickness. Other appropriate indices included the 

Radius of Curvature (both R112 and R218), SCI12, 

and SD12, but unlike R212 and SCIm8, these were 

dependent on AC thickness.

To confirm the adequacy, applicability, and validity of the indices, Monte Carlo simulations were 

conducted using a JULEA-generated database 

of 15,000 pavement structures covering a wide 

range of layer moduli and thicknesses.(3) Surface 

deflections and horizontal strains at the bottom of the AC layer were computed for each 

pavement structure. The database was first used 

to identify the most sensitive pavement properties that affect the critical responses. It was 

then used to identify the most sensitive index 

that correlated well with the critical responses. 

From the results of the Monte Carlo simulations, SCI8 and SCI18 were initially recommended 

for pavement structures with known AC layer 

thicknesses, while R212 was recommended for 

pavement structures with unknown AC layer 

thicknesses.

To ensure the adequacy of the recommended 

indices, the TSD accuracy and precision results 

from the MnROAD field trials were used to 

assess their practicality and robustness. Only 

TSD data were used since the RWD deflections 

were limited to only two points. For accuracy, 

the indices were compared with those from the 

deflection basins measured with geophone 3 

at the MnROAD cells. Accuracy was evaluated 

in terms of the percentage of difference for 

the deflection indices. For precision, SEE values 

and ranges were estimated for each index and 

each MnROAD cell. The median coefficient of 

variation (COV) (ratio between median SEE and 

median range) was used to quantify the precision of each deflection index. 

The accuracy and precision results were then 

combined in a plot segmented into four quadrants 

based on 15 percent median difference and 15 percent median COV limits (see figure 10). The most 

robust indices are those in the lower left quadrant 

shaded in green, and they include four DSIs, TS4, 

and AUPP. The indices in the upper left quadrant 

(e.g., TS8, TS12, and SCI12) may also be considered 

as reasonably precise but not as accurate as 

those in the lower left quadrant. In addition, the 

three radii of curvature indices are in the upper 

right quadrant shaded in red; therefore, they 

are considered less accurate and less precise as 

compared to those in the lower left quadrant.

The recommended indices were then ranked 

according to overall performance. The device 

precision, device accuracy, and model uncertainties (ratio of SEE to mean range of calculated

Figure 10. Overall field performance of indices.
9

strains) were assigned values of 1 (poor) when 

their values were greater than 20 percent, 

3 (fair) when their values were between 10 and 

20 percent, and 5 (good) for values less than 

10 percent. Similarly, the R2 values were assigned 

values of 1 (poor) for values less than 0.90, 

3 (fair) for values between 0.90 and 0.95, and 

5 (good) for values above 0.95. Equal weights 

(25 percent) were assigned to the four parameters to obtain an overall performance score for 

the promising indices. 

The complete set of recommended indices 

are listed in table 3. As shown, DSI4 – 12 (difference between deflections at 4 and 12 inches 

(101.6 and 304.8 mm) from load), which was 

formulated as part of the study, was found to 

be the most appropriate index, regardless of AC 

thickness, and hence it is recommended for use 

in network-level PMS applications. SCI12 (difference between deflections at 0 and 12 inches 

(0 and 304.8 mm) from load) performed nearly 

as well, and hence it could also be considered. 

Regardless of the index selected, it is important 

for users to understand the range of conditions 

that affected the accuracy and precision evaluation of the TSD and hence the aptness of the 

recommended indices.

Implementation of Findings

The identification of indices that best relate 

deflections measured by TSDDs to horizontal

strains at the bottom of the AC layer is an 

important step toward the use of these devices 

and indices in support of network-level PMS 

applications, but additional steps are needed. 

For example, how are the horizontal strains 

computed at the bottom of the AC layer based on 

the measured deflection(s) to be used to assess 

the structural capacity of pavements? This and 

other network-level PMS implementation considerations are addressed in this section. 

While different approaches are possible, the 

one illustrated in figure 11 is recommended for 

network-level PMS applications and decisionmaking. In this approach, the decisions based 

on the traditional pavement condition metrics 

are confirmed and/or adjusted based on the 

TSDD measurements. This enables the pavement community an opportunity to gradually 

implement TSDD measurements while avoiding 

abrupt changes to their institutional approaches.

To deliver a robust system, the level of sophistication of the analysis should be balanced with 

the uncertainties of the TSDDs. Accordingly, 

Table 3. Study recommended deflection indices. 

AC Thickness Index

Device 

Precision 

(Percent)

Device Accuracy 

(Percent)

Model 

Uncertainty 

(Percent) Model R2

3–6 inches

DSI4 – 12 9 8 16 0.88

SCI12 11 18 15 0.90

DSI4 – 8 10 12 13 0.92

TS4 14 11 13 0.91

Greater than 

6 inches

DSI4 – 12 9 8 13 0.97

DSI8 – 12 9 7 12 0.98

SCI12 11 18 14 0.96

DSI4 – 8 10 12 17 0.95

TS8 9 15 17 0.94

TS12 10 21 17 0.96

AUPP 11 13 15 0.97

Unknown

DSI4 – 12 9 8 22 0.97

SCI12 11 18 20 0.97

1 inch = 25.4 mm
10

factors such as accuracy and precision of 

measurements, TSDD operation parameters, 

and availability of additional information, such 

as pavement structure, traffic, and loadings, 

were considered in proposing the incorporation of TSDD measurements into network-level 

PMS applications. In turn, the incorporation of 

TSDDs into network-level PMS applications 

can be summarized in the following steps:

1. Calculate representative indices for estimating structural condition of pavement: As 

stated earlier, the most feasible parameters 

are DSI and SCI considering fatigue cracking 

as the critical parameter.

2. Estimate horizontal strains at bottom of 

AC layer: This can be done using recommended or user selected deflection index 

(see table 3).

3. Adjust the estimated strains to a standard 

temperature: The strains computed in step 2

need to be corrected to a standard reference temperature for consistent evaluation and tracking of the deflection indices 

over time. Assuming a standard reference 

temperature of 70 oF (21.11 oC), the recommended approach, developed as part of the 

study, is to: (1) compute a temperature correction factor based on the temperature at 

time of the TSDD field measurements and 

a reference temperature of 70 oF (21.11 oC),

(2) compute the AC dynamic modulus 

based on the strains computed in step 2 and 

the AC layer thickness, (3) compute the AC 

dynamic modulus at the reference temperature of 70 oF (21.11 oC), and (4) compute the 

temperature corrected strains using the AC 

modulus at the reference temperature of 

70 oF (21.11 oC). Detailed steps are provided 

in the corresponding report.(1)

4. Establish structural adequacy of pavements 

using temperature corrected strain: Ideally, it 

is desirable to be able to determine whether 

different segments of a pavement network 

are candidates for either preservation, 

maintenance, rehabilitation, or reconstruction or whether they are adequate in their 

current condition. At a minimum, the algorithm associated with this step needs to be 

able to provide information on whether the 

pavement is structurally sound for the anticipated traffic or not. The initial work toward 

this goal can be found in Thyagarajan et al., 

among others.(4,5) Abdallah et al. suggested probabilistic methods for this purpose 

Figure 11. Idealized PMS containing a TSDD structural evaluation component.
11

as applied to FWD using artificial neural 

networks.(6) These and similar work by other 

researchers should be pursued.

Besides these four steps, another critical 

consideration in the incorporation of TSDDs 

into network-level structural evaluations is 

the spatial averaging of the data. For effective 

utilization, spatial statistical analysis and segmentation of the TSDD data are necessary. A 

statistical tool is needed that enables pavement 

engineers to distinguish changes in the road 

segments due to either changes in the pavement 

structure or the deterioration of the pavement 

sections and considers the capabilities of the 

TSDD as well as the condition and nature of the 

pavement structures. In the short term, a probabilistic structural analysis can be incorporated 

for segmentation of the roads at the network 

level. In the medium and long terms, a dynamic 

and adaptive optimization can be added to 

assist decisionmakers.

Another important consideration is the requirement for additional information to enable more 

rigorous structural evaluations. This information 

includes best estimates of pavement structures 

(and in particular the AC layer thickness), functional condition of the pavements (including IRI 

and extent of cracking) to check and/or to help 

rationalize the results, best estimates of present and future traffic, and ambient condition at 

the time of testing. It is also desirable that tools 

be added to the TSDDs by the manufacturers 

and/or owners to measure these data.

Conclusions and Other 

Recommendations

This section summarizes the major findings, 

conclusions, and recommendations of the 

FHWA-sponsored study to establish a reliable 

measure of pavement structural condition based 

on traffic speed deflection technology. TSDD 

testing has advanced over the past decade, and 

the technology of these devices—the RWD and 

TSD—provides reasonable accurate and precise 

pavement response measurements. Data analyses have shown that indices that can be derived 

from TSD measurements provide a robust 

assessment of pavement structural conditions at 

the network level. Improvements in the number 

of sensors and their locations are needed to 

use the recommended analysis methodologies 

with the RWD, but these should not be difficult 

to achieve.

The initial recommended implementation steps 

were detailed in the previous section, but these 

steps need to be taken from concept to full 

implementation. Similarly, the validation and/or 

calibration of the recommended deflection 

indices as well as implementation procedures 

need to be performed using field data collected on highway agency networks. Significant 

equipment improvements are also possible. 

For example, measurements of deflection 

parameters at two or more points (i.e., basins) 

as well as the load characteristics applied to 

the pavement during testing will enable more 

robust analyses. 

The data collected by TSDDs were averaged over distance. Averaging was done over 

0.1-mi (0.161-km) intervals for the RWD and 

over 32.8-ft (10-m) intervals for the TSD. The 

shorter the averaging distance is, the more 

detailed the analysis will be. Averaging is necessary for SHAs to work with a manageable 

amount of data. Accordingly, in the short term, 

it is desirable for manufacturers to report not 

only the mean deflection parameters but also 

other statistical information so that analysts 

can judge the level of variability associated 

with the data. In the medium term, the level of 

uncertainties in the measured deflection parameters should be verified through independent 

research. In the long term, it is desirable that 

averaging be done as part of the analysis and 

not the data collection.

In addition, several future research studies are 

recommended, including (1) confirming the 

predictive power of the recommended deflection indices through the use of measurements 

taken by strain gauges at the bottom of the 

AC layer during TSDD loadings, (2) expanding and validating the prediction of subgrade 

strain to complement horizontal strains at the 

bottom of the AC layer, (3) applying TSDDs to 

PCC pavements to evaluate their applicability to 

network-level pavement evaluations, including
12

Researchers—This study was performed by Amec Foster Wheeler Environment & Infrastructure, Inc., 

in conjunction with The University of Texas-El Paso and University of Nevada-Reno. The principal 

investigator was Dr. Gonzalo Rada, P.E.

Distribution—The TechBrief is being distributed according to a standard distribution. Direct distribution is being made to the Divisions and Resource Center.

Availability—The TechBrief may be obtained from the FHWA Product Distribution Center by e-mail to 

report.center@dot.gov, fax to (814) 239-2156, phone to (814) 239-1160, or online at http://www.fhwa.dot.

gov/research.

Key Words—Pavement structural health monitoring, Traffic speed deflection devices, Field evaluation, 

Analytical model, Pavement management system.

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MARCH 2016 FHWA-HRT-15-075

HRDI-20/03-16(WEB)E

load transfer and joint condition, (4) conducting 

a more indepth study of the impact of ambient and pavement structure conditions on 

the deflection measurements for more robust 

guidelines, and (5) determining methodologies 

for the development of structural performance 

curves for prediction of future condition from 

TSDD-derived structural indices measured 

over time.

References

1. Rada, G.R., Nazarian, S., Visintine, B.A., 

Siddharthan, R., and Thyagarajan, S. 

(2015). Pavement Structural Evaluation 

at the Network Level, Report No. FHWAHRT-15-074, Federal Highway Administration, Washington, DC.

2. Siddharthan, R.V., Yao, J., and Sebaaly, P.E. 

(1998). “Pavement Strain From Moving 

Dynamic 3-D Load Distribution,” Journal of 

Transportation Engineering, 124(6), 557–566.

3. Uzan, J. (undated). JULEA: Jacob Uzan 

Layered Elastic Analysis, Technion University, Haifa, Israel. 

4. Thyagarajan, S., Sivaneswaran, N., Petros, 

K., and Muhunthan, B. (2011). Development 

of a Simplified Method for Interpreting 

Surface Deflections for In-Service Flexible 

Pavement Evaluation, 8th International 

Conference on Managing Pavement Assets, 

Santiago, Chile.

5. Thyagarajan, S., Sivaneswaran, N., and 

Petros, K. (2015). “Incorporating Traffic 

Speed Deflection Devices in Structural 

Evaluation of Flexible Pavement: Methodologies for Pavement Management Application,” Transportation Research Record 

2523, 72–79, Transportation Research Board, 

Washington, DC.

6. Abdallah, I., Melchor-Lucero, O., Ferregut, 

C., and Nazarian, S. (2000). Development 

of a Comprehensive, Rational Method 

for Determination of Remaining Life of 

an Existing Pavement, Report No. TX-99/

1711-2, Texas Department of Transportation, 

Austin, TX.