Effects of Seasonal and Diurnal 

FWD Measurements on LTE 

of JPCP—LTPP SMP Data

FHWA Publication No.: FHWA-HRT-22-094

FHWA Contact: Deborah Walker (ORCID: 0000-0002-3684-5190), HRDI-30, 

Long-Term Infrastructure Performance Team, 202-493-3068, 

deborah.walker@dot.gov.

Researchers: Hamad Bin Muslim and Syed W. Haider, Civil and 

Environmental Engineering Department, Michigan State University.

This report won the graduate category of the 2019–2020 Data Analysis 

Student Contest sponsored by the Federal Highway Administration’s 

Long-Term Pavement Performance (LTPP) program and American 

Society of Civil Engineers. This report, entitled Effects of Seasonal and 

Diurnal FWD Measurements on LTE of JPCP—LTPP SMP Data, analyzes 

the effects of seasonal and diurnal falling weight deflectometer (FWD) 

measurements on the load transfer efficiency (LTE) of jointed plain 

concrete pavements and develops general guidelines for conducting 

FWD deflection measurements.

INTRODUCTION

The structural capacity of pavements is typically evaluated by conducting 

a nondestructive test such as a falling weight deflectometer (FWD). The 

deflections that are measured with an FWD device are then analyzed to obtain 

the layer moduli values, such as the elastic modulus of a portland cement 

concrete (PCC) slab and the modulus of subgrade reaction (k-value) in case 

of rigid pavements. Based on the different deflection-based parameter values, 

the current pavement condition is then characterized. However, seasonal 

and diurnal temperature variations affect the measured deflections on rigid 

pavements. Curling, which is caused by the temperature differential between a 

slab’s surface and bottom side, can occur and subsequently influence the FWD 

deflection measurements. Thus, if a midslab deflection basin is obtained when 

the slab is curled down (i.e., the slab’s surface is warmer than its bottom side) 

or corner deflections are obtained when the slab is curled up (i.e., the slab’s 

surface is cooler than its bottom side), the slab may be unsupported. Such 

conditions will result in more considerable deflections.

Load transfer efficiency (LTE) is one of the critical elements that can be 

estimated across joints and cracks by using FWD deflection measurements 

while evaluating the condition of rigid pavements. Seasonal and diurnal 

temperature variations also affect the joint and crack behavior in rigid 

pavements. Elevated temperatures cause the concrete slabs to expand, and this 

PCC slab expansion, coupled with slab curling, results in locked-up joints. 

Deflection testing that is conducted on locked-up joints will result in lower and 

equal joint deflections and higher LTEs, which is misleading regarding a joint’s 

overall load transfer capabilities.

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

https://highways.dot.gov/research
2

The primary objectives of this TechBrief are to evaluate 

the effects of seasonal and diurnal FWD measurements 

on the LTE of jointed plain concrete pavement (JPCP) 

sections and to develop general guidelines for conducting 

FWD deflection measurements. The data from the JPCP 

sections of the Long-Term Pavement Performance 

(LTPP) Seasonal Monitoring Program (SMP) study were 

synthesized and analyzed to accomplish these objectives.

Environmental Effects on a PCC Slab

The environmental effects on a PCC slab can unfold 

in two ways: deterioration of the concrete itself, which 

is related to concrete mixture design and construction 

and is not the focus of this TechBrief; and, volumetric 

changes that cause the concrete slab to change size 

either horizontally or vertically. The latter is called 

curling, which develops because of differential volume 

changes across the slab thickness. Temperature gradients 

(i.e., the temperature difference between the surface 

and the bottom side of a PCC slab at a particular time 

of the day) cause the slab to curl up or down during 

the nighttime (negative gradient) and daytime (positive 

gradient), respectively (see figure 1). Both upward and 

downward curling increases as the temperature gradients 

increase (Siddique et al. 2005). This curling results in 

loss of support at the slab’s corners or its center coupled 

with the self-weight of the slab, and vehicle loads 

contribute to early-age slab cracking or even slab failure 

(Choubane and Tia 1995; Jeong and Zollinger 2005; 

Siddique et al. 2005). Historically, the thermal stress 

distribution had been considered linear across the slab 

thickness (Westergaard 1927). However, current research 

Figure 1. Diagram. Location of tensile stress under temperature- and moisture-related curvatures.

© 2021 H. Muslim.

T = temperature.
3

shows that the gradient can be nonlinear (Choubane and 

Tia 1992; Jeong and Zollinger 2005; Siddique et al. 2005). 

A drawback of considering a nonlinear thermal gradient 

is that it results in under- and overestimating the stresses 

during daytime and nighttime, respectively (Choubane 

and Tia 1995). The tensile stresses can be as high as 

5.5 times larger on the edge and as high as 8.8 times 

larger at the corner of the slab than stresses induced by 

a standard axle, once negative gradients occur (Trujillo 

and Guettero 2019).

Load Transfer at Joints

The doweled transverse joint of a JPCP pavement 

undergoes deflection when wheel loads are applied. 

Both sides of the joint deflect because of the transfer of 

the applied load from the loaded to the unloaded slab. 

This load transfer occurs due to the load transfer devices 

(i.e., the dowel bars) in a JPCP. The presence of load 

transfer devices ensures lesser deflection and stresses 

in the loaded slabs as compared to undoweled slabs or 

slabs with free edges (McCracken 2008). The term LTE 

is used to express a joint’s ability to transmit part of the 

applied load from the loaded slab to the adjacent unloaded 

one in a JPCP (Ioannides and Korovesis 1992). Several 

formulations are used to calculate the LTE for a joint. 

However, the deflection-based index is widely used and is 

accepted by the American Association of State Highway 

and Transportation Officials (AASHTO) pavement design 

guide (AASHTO 1993). The standard way to calculate 

LTE, which is also used by the LTPP program (Elkins et 

al. 2018), is the ratio of the unloaded to the loaded vertical 

displacements measured at a joint on the slab’s surface. 

As discussed in the Introduction, slab curling is also 

known to affect the joint and crack behavior of PCC slabs. 

Expansion of a slab in elevated temperatures coupled 

with slab curling may cause a joint to lock. Such joint 

locking results in reduced or equal joint deflections and 

higher LTE values, whereas the opposite is true in low 

winter temperatures, during which the joints open as a 

result of the slab’s contraction. A study that evaluated 

LTE measurements made the following conclusions 

(Shoukry et al. 2005):

• LTE measurements depend on many factors, including 

load position, testing time, slab temperature, and load 

transfer device.

• LTE measurements are significantly affected by testing 

time and season. For the same joint, LTE measured in 

winter was less than that measured in summer.

• LTE measurements are affected by joint opening 

changes, daily and seasonally, as the ambient 

temperature changes. As the amount of joint 

opening increases due to slab contraction during 

the winter, the measured LTE generally decreases.

• LTE measurements increase as a slab’s 

temperature rises.

LTPP SMP Study

To evaluate the effects of seasonal and diurnal 

FWD-based measured deflections on the computed 

load transfer capacity of joints, identification of an 

adequate dataset that contained multiple seasonal and 

diurnal FWD measurements was crucial. Such extensive 

FWD data were identified in the SMP study of the 

LTPP program (FHWA 2019), which was designed to 

evaluate the influence of temporal changes in pavement 

structural characteristics attributable to diurnal and 

seasonal variations in temperature and moisture 

(Elkins et al. 2018). As the primary measure of a 

pavement’s structural characteristics, multiple seasonal 

and diurnal FWD deflection measurements are peculiar 

to the data contained in the LTPP SMP study. Based 

on the unique nature of the contained data, the SMP study 

was used to evaluate the effects of seasonal and diurnal 

FWD measurements on the JPCP sections included in 

the study.

DATA SYNTHESIS

This section describes the identified data elements, their 

availability, and the extent to which these data were used 

to accomplish the study’s objectives.

Data Elements

Table 1 presents a summary of the data elements 

identified for the analysis:

• Pavement cross sections, age, and material types 

for all layers of JPCP sections.

• Temperature and precipitation data from the climatic 

module and information on the climatic regions.

• Monitored FWD deflection data with air and surface 

temperatures, temperature gradients, testing dates, 

and timings.

• Monitored performance data (LTE values) over time.
4

Database Development

The required data were obtained from the LTPP database 

standard release 33.0 and represented the most recent 

and up-to-date available data (FHWA 2019). The JPCP 

sections included in the SMP study were assigned a 

unique identification (ID) by combining the State code 

with its Strategic Highway Research Program (SHRP) 

ID. Multiple data buckets containing the desired data 

variables were downloaded by using the online InfoPave® 

features (Elkins et al. 2018). Microsoft® Access® was 

used to organize the downloaded data elements into 

various data tables to create a relational database.

Temperature Gradients

The table MON_DEFL_TEMP_DEPTHS of the LTPP 

program contains the depths at which temperatures are 

measured during FWD testing (Elkins and Ostrom 2019). 

Table 1. LTPP program database tables used to extract data elements (Elkins et al. 2018).

Data Elements LTPP Table Description

Layer number

Layer type

Representative thickness

Material types

SECTION_LAYER_STRUCTURE

This table contains a consolidated set of 

pavement layer structure information for 

all LTPP test sections.

State code

SHRP ID

Experiment name and number

Assign date

Construction number 

Construction number change reason 

De-assign date

EXPERIMENT_SECTION

This table is the master control table for all 

test sections and project sites included in 

the LTPP database.

Precipitation and climatic regions TRF_ESAL_INPUTS_SUMMARY

The table includes average annual 

precipitation and freeze index, LTPP 

experimental climate region, and the 

source for this classification.

Test date

Test time

Deflection unit identifier

Point location

Drop height and load

Peak deflection sensor

Layer temperature data with depths

Lane number

Air and pavement surface temperatures 

measured by instruments on the FWD

MON_DEFL_DROP_DATA

The table contains peak deflection, 

peak load, and other drop-specific 

FWD measurements.

MON_DEFL_TEMP_DEPTHS

This table contains the depth at which 

temperature gradient data are collected 

during FWD testing.

MON_DEFL_TEMP_VALUES

This table includes in-pavement 

temperature gradient data obtained 

during FWD testing.

MON_DEFL_LOC_INFO This table contains information specific to 

each point at which testing was conducted.

LTE MON_DEFL_LTE

This table contains the LTE computed 

parameter. LTE is computed from FWD 

measurements at transverse joints and 

cracks on PCC pavements.

ID = identification; SHRP = Strategic Highway Research Program.

These temperatures are different than the surface 

temperatures measured by sensors attached to the FWD. 

Generally, a minimum of three temperature measurements 

that correspond to the top, middle, and bottom of the PCC 

layer are taken. However, in some instances, the holes 

for taking temperature measurements were found to be 

drilled entirely through the bound surface layer into the 

base material (Elkins et al. 2018). Therefore, the data 

found in the MON_DEFL_TEMP_DEPTHS table were 

matched with the representative thickness of the slab. 

Only the temperature measurements that were obtained 

within ±1.25 inches of the top surface or bottom side of 

the PCC layer were used to calculate the temperature 

gradients (i.e., top minus bottom) for analysis.

Maintenance Categories

The maintenance history data of the available JPCP 

sections were categorized based on the anticipated effect 
5

Table 2. Available number of LTE (before and after joint) values by climatic regions.tables used to extract data 

elements (Elkins et al. 2018).

FWD PASSES DF DNF WF WNF

1 114 (2) 158 (2) 316 (6) 270 (6)

2 88 (2) 120 (2) 228 (5) 168 (3)

3 30 (2) 46 (2) 64 (5) 36 (3)

4 2 (1) 2 (1) 2 (1) 4 (2)

Note: Values in parentheses are the number of sections.

of these maintenance events on FWD deflections. Four 

categories were used, as follows:

• Category 0: No maintenance at all.

• Category 1: Maintenance works with no significant 

effects on FWD measurements.

• Category 2: Localized patchwork.

• Category 3: Maintenance works with significant 

effects on pavement structure and response.

Available Data and Extents

FWD pass data were obtained for 16 JPCP sections 

identified in the LTPP SMP study (FHWA 2019). An 

FWD pass serves to distinguish multiple runs of the 

same lane number on the same day (Elkins et al. 2018). 

Table 2 shows the available LTE values for multiple FWD 

passes on the same day for the available JPCP sections 

within each climatic region (i.e., dry, freeze (DF); dry, 

no freeze (DNF); wet, freeze (WF); and wet, no freeze 

(WNF)) of the SMP study. More data on the wet regions 

were available because there were more JPCP sections on 

wet climates. Table 2 includes the data for both locations 

(i.e., before and after the joint), where deflections were 

measured to calculate the LTE. The lower of the two LTE 

values was used in the analysis.

Data Assessment

Figure 2 illustrates the effects of diurnal (i.e., by FWD 

pass) and seasonal FWD deflection measurements on the 

obtained LTE values in different climatic zones for all 

JPCP pavement sections in the SMP study. Figure 2 also 

presents the distribution of the available data within each 

climatic region, and it shows that LTE values are affected 

by diurnal and seasonal measurements. Higher LTE values 

are expected at higher temperatures due to slab expansion 

and joint locking, whereas lower LTE values correspond 

to slab contraction occurring during lower temperatures. 

Table 3 shows the descriptive statistics for LTE values for 

the available JPCP sections and the available number (N) 

of the LTE measurements within each climatic zone. A 

higher variation evident from the larger standard deviation 

(Std. Dev.) was seen in the freeze regions. Therefore, it is 

essential to consider the diurnal and seasonal temperatures 

for such measurements for rigid pavements.

Figure 2. Graphs. Assessments of available LTE data.

© 2021 H. Muslim.

A. LTE by FWD pass. B. LTE by FWD measurement month.
6

Figure 2. (Continued) Graphs. Assessments of available LTE data.

© 2021 H. Muslim.

DATA ANALYSIS AND RESULTS

As noted in the Introduction, temperature variations can 

affect the joint and crack behavior in rigid pavements. 

Warmer temperatures cause the slabs to expand, and when 

the expansion is coupled with slab curling, the joints 

might lock. Therefore, if deflection testing is conducted at 

the joints when they are locked, the joint deflections will 

be lower. Resultantly, the determined LTEs will be higher, 

which is misleading regarding the joint’s overall load 

transfer capabilities. The effects of temporal (seasonal and 

diurnal) variations on LTE were investigated using data 

extracted from the LTPP SMP study for the JPCP sections.

Steps for Data Analysis

The analysis involved conducting the following steps:

1. Identifying the variables needed to perform the 

analysis, including the pavement structure details, 

LTE values, FWD measurement dates and timings, 

maintenance history, pavement surface temperature, 

air temperature, and temperature gradient 

measurements.

Table 3. Descriptive statistics of LTE values.

Climatic Region N Mean Std. Dev. Minimum Q1* Median Q3* Maximum

DF 234 58.9 27.5 15.2 30.7 66.2 85.3 96.8

DNF 326 69.2 17.0 25.0 53.8 71.6 84.2 96.9

WF 610 70.6 26.0 16.0 44.3 85.0 91.5 98.2

WNF 478 75.2 19.0 18.4 57.8 86.7 90.3 97.0

Q1 = first quartile; Q3 = third quartile.

Note: Values in parentheses are the number of sections.

2. Identifying the JPCP pavements sections in the LTPP 

SMP study within each of the four climatic regions 

(FHWA 2019). Note that the climatic regions are 

defined in terms of temperature (i.e., freeze/no freeze) 

and moisture (i.e., wet/dry).

3. Obtaining the LTE values, the date and time of 

FWD measurements, the air and pavement surface 

temperatures measured at the time of FWD testing, 

and the temperatures measured at different depths for 

temperature gradient measurements.

4. Extracting and then categorizing the maintenance 

history details (i.e., construction numbers and types 

of treatment) for all the sections in the database.

5. Arranging the data in a relational database.

6. Inspecting the data by using a histogram and boxplot 

to identify outliers.

7. Using two quantifying factors: measurement month 

discretized into four seasons (i.e., levels) to study 

the seasonal effects, and measurement time with two 

levels (before noon and afternoon) to investigate the 

diurnal effects.

C. Histogram—available LTE values. D. Box plot—available LTE values.
7

8. Conducting an analysis of variance (ANOVA) 

of the JPCP sections within each climatic region 

to investigate the temporal effects on the joint 

LTE values.

Figure 3 demonstrates the normality of the data for the 

DNF climatic region that was used in the analysis. The 

satisfaction of the normality assumptions is essential to 

draw meaningful conclusions from the ANOVA analysis. 

Similarly, the data were transformed adequately for 

the rest of the climatic regions to ensure the normality 

assumptions’ satisfaction.

Discussion on Results

Table 4 presents the ANOVA analysis results for the 

DF climatic region. The results show that the FWD 

measurement season significantly affected the LTE values 

based on a type I error rate of 5 percent (α = 0.05). In 

contrast, the FWD measurement time had no significant 

effect on the LTE values at the same error rate. The results 

also show that the interaction between the two factors 

(i.e., season and time) contributed significantly to the 

LTE values’ variations. Thus, looking at the interaction 

effects rather than the main effects is essential. Figure 4-A

shows that the mean LTE values in the summer varied 

from 68 percent for the before-noon measurement to 

85 percent for the afternoon measurement. In addition, 

the LTE values differed in the spring and fall within the 

before-noon and afternoon measurements; however, the 

difference was not as significant as the summer variations. 

Figure 4-B shows that the difference of mean LTE 

values between any pair of seasons and time interactions 

involving the summer is substantial. Thus, the results 

suggest that summer LTE values are significantly higher 

than the values obtained in the other seasons, irrespective 

of the time of the day, with a more significant difference 

in the afternoon measurements.

Figure 3. Graphs. Evaluating normality of the LTE data—DNF climatic region.

© 2021 H. Muslim.

A. Normal probability plot.

C. Histogram of residuals. D. Residual versus observation order.

B. Residual versus fitted values.
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Table 4. ANOVA results for LTE values—DF climatic region.

Source DoF Seq SS Contribution (percent) Adj SS Adj MS F Value p Value

Season 3 20,540 12.94 20,158 6,719.5 25.52 0.000*

Time 1 1,010 0.64 1,031 1,031.3 3.92 0.049

Maintenance category 2 77,063 48.56 73,316 36,658.2 139.22 0.000

Season × time 3 3,743 2.36 3,743 1,247.5 4.74 0.003*

Error 214 56,350 35.51 56,350 263.3 — —

Lack of fit 14 16,188 10.20 16188 1156.3 5.76 0.000

Pure error 200 40,163 25.31 40163 200.8 — —

Total 223 158,707 100.00 — — — —

—No data.

*Significant factor or interaction.

Adj = adjusted; DoF = degrees of freedom; Seq = sequential; SS = sum of squares; MS = mean squares.

Table 5. ANOVA results for LTE values—DNF climatic region.

Source DoF Seq SS Contribution 

(percent) Adj SS Adj MS F Value p Value

Season 3 547,985,885 32.70 520,306,982 173,435,661 56.42 0.000

Time 1 33,623,209 2.01 17,249,536 17,249,536 5.61 0.018

Maintenance category 1 87,039,916 5.19 94,121,166 94,121,166 30.62 0.000

Season × time 3 32,552,731 1.94 32,552,731 10,850,910 3.53 0.015*

Error 317 974,519,223 58.16 974,519,223 3,074,193 — —

Lack of fit 6 179,477,449 10.71 179,477,449 29,912,908 11.70 0.000

Pure error 311 795,041,774 47.44 795,041,774 2,556,404 — —

Total 325 1,675,720,963 100.00 — — — —

—No data.

*Significant factor or interaction.

To further explain the higher LTE values during the 

summer, figure 4-C shows the 95 percent confidence 

interval of the pavement surface and air temperatures 

at the time of FWD measurements. There was a clear 

difference between the temperatures recorded while 

conducting FWD measurements before noon and during 

the afternoon. This difference suggests that temperatures 

higher than 80 °F should be avoided while conducting 

FWD testing on joints because such elevated temperatures 

significantly increase the observed LTE.

Although the maintenance category can significantly 

influence the LTE values, it was used as a blocking factor. 

Blocking is a technique that can increase the precision in 

an analysis or an experiment by reducing the experimental 

error variance. It is achieved by considering factors 

believed to affect the response but not considered to be 

of primary importance in the analysis. Therefore, no 

further discussion related to the maintenance category 

will be presented beyond this point.

The results of a similar ANOVA analysis for the DNF 

climatic region show that the interaction of the two 

factors—the FWD measurement season and time—have 

more influence on the LTE values than the individual 

factors (see table 5). The interaction means plot did not 

show that there was a significant difference between LTE 

values determined from deflections measured before noon 

and during the afternoon (see figure 5-A). The overall 

mean LTE values for the DNF climatic region were higher 

than for the DF climatic region. A possible explanation 

for higher LTE values, with no significant difference 

between the two times of the day, can be the higher range 

of measurement temperatures. The temperature ranges 

within the DNF region are higher than 75 °F (figure 5-B). 

Therefore, temperatures higher than 75 °F should be 

avoided while measuring LTE at joints to eliminate the 

effect of locked joints on the observed joint deflections 

and prevent overestimation of LTE.
9

A. Interaction means plot.

B. Mean difference of LTE values with 95 percent 

confidence interval for season and time interaction.

C. Pavement surface and air temperatures during 

FWD measurements.

AN = afternoon; 

BN = before noon.

Figure 4. Graphs. ANOVA results—DF climatic region.

© 2021 H. Muslim.

Figure 5. Graphs. ANOVA results—DNF climatic region.

© 2021 H. Muslim.

A. Interaction means plot.

B. Pavement surface and air temperatures 

during FWD measurements.
10

Table 6. ANOVA results for LTE values—WF climatic region.

Source DoF Seq SS Contribution 

(percent) Adj SS Adj MS F Value p Value

Season 3 756,157,660 13.11 680,113,939 226,704,646 34.88 0.000*

Time 1 154,530,676 2.68 43,189,635 43,189,635 6.65 0.010*

Maintenance category 2 1,058,360,121 18.35 1,062,049,115 531,024,558 81.71 0.000

Season × time 3 4,293,469 0.07 4,293,469 1,431,156 0.22 0.882

Error 584 3,795,286,065 65.79 3,795,286,065 6,498,778 — —

Lack of fit 14 414,978,033 7.19 414,978,033 29,641,288 5.00 0.000

Pure error 570 3,380,308,032 58.60 3,380,308,032 5,930,365 — —

Total 593 5,768,627,991 100.00 — — — —

—No data.

*Significant factor or interaction.

Table 7. ANOVA results for LTE values—WNF climatic region.

Source DoF Seq SS Contribution 

(percent) Adj SS Adj MS F Value p Value

Season 3 47,986 33.88 49,449.3 16,483.1 86.60 0.000*

Time 1 3,256 2.30 3,217.5 3,217.5 16.91 0.000*

Maintenance category 1 352 0.25 342.5 342.5 1.80 0.180

Season × time 3 1,161 0.82 1,160.7 386.9 2.03 0.108

Error 467 88,882 62.75 88,882.4 190.3 — —

Total 475 141,637 100.00 — — — —

Pure error 570 3,380,308,032 58.60 3,380,308,032 5,930,365 — —

Total 593 5,768,627,991 100.00 — — — —

—No data.

*Significant factor or interaction.

The results of the ANOVA analysis for wet regions show 

that the main effects of the two factors, namely season 

and time, are significant contributors to the variations 

in LTE, whereas their interaction is not (see table 6 and 

table 7). The main effects plot in figure 6-A shows that 

the mean LTE value during the summer is significantly 

higher than during other seasons. Figure 6-C shows 

that any difference involving LTE obtained from FWD 

conducted during the summer season is significant. For 

the WNF climatic region, the LTE values were statistically 

different between all the seasons (figure 6-B and 

figure 6-D). However, the difference between the winter 

and spring LTE values (>15 percent) and the summer and 

spring LTE values (>20 percent) can only be termed as 

practically different.

The elevated summer season LTE values in the WF 

climatic region can be attributed to the recorded hightemperature range, that is, higher than 75 °F (80–84 °F) 

during the FWD measurements (figure 7-A). Similarly, 

the difference between the seasonal LTE values in the 

WNF climate is also evident from the temperature ranges 

during FWD testing (figure 7-B). Again, the higher 

summer LTE values are attributable to the elevated 

temperatures recorded during FWD measurements. In 

addition, an increase in the winter mean LTE values 

observed in the WF climatic region can be explained by 

the joints’ resistance to movement due to extremely low 

temperatures, that is, below 35 °F (figure 7-B). Such 

low temperatures should be avoided while conducting 

FWD tests.

The diurnal trends in both the WF and WNF climates 

(figure 6-A and figure 6-B, respectively) indicated that 

the before-noon measurements result in a lower LTE 

value than the afternoon measurements. Figure 7-C and 

figure 7-D show the diurnal temperature ranges at the 

time of FWD measurements in the wet climatic regions. 
11

In the WF region, the before-noon air temperatures 

are not very different from the afternoon temperatures; 

however, there is a significant difference between the 

corresponding pavement surface temperature ranges. 

Such a difference in the pavement surface temperature 

causes the LTE values that are measured before noon to 

be lower than the afternoon LTE values. The difference in 

the diurnal temperature ranges in the WNF climatic region 

explains the lower before-noon LTE values as opposed to 

afternoon LTE values.

In general, FWD measurements conducted before noon 

result in lower LTE values than those measured in the 

afternoon, irrespective of the climatic region. Therefore, 

it is beneficial to perform deflection measurements on 

joints for LTE determination before noon when slabs are 

flatter. Figure 8 shows the histogram of the temperature 

gradients data available in the LTPP SMP study for 

the JPCP sections (FHWA 2019). A zero or near-zero 

temperature gradient would be ideal for measuring 

deflections at joints for LTE determination, because 

such LTE values will depict a joint’s actual load transfer 

capacity. For approximately 60 measurements in the 

data, the temperature difference was between ˗1 and 1 °F. 

The time of these measurements ranged from 8 a.m. to 

around 1 p.m. Hence, the available data also suggest that 

diurnally before-noon measurements are ideal for finding 

a zero gradient temperature condition, which can help 

identify the condition of the joints accurately.

A. Main effects plot for LTE—WF 

climatic region.

C. Seasonal mean difference of LTE 

values with 95 percent confidence 

interval—WF climatic region.

B. Main effects plot for LTE—WNF 

climatic region.

D. Seasonal mean difference of LTE 

values with 95 percent confidence 

interval—WNF climatic region.

Figure 6. Graphs. ANOVA results—WF and WNF climatic regions.

© 2021 H. Muslim.
12

Figure 7. Graphs. Seasonal and diurnal pavement surface and air temperatures.

© 2021 H. Muslim.

The information presented in the Discussion on Results 

section could be used to suggest guidelines for FWD 

deflection measurements based on ambient temperature 

ranges. Although pavement temperature is known to 

have more effect on LTE than the ambient temperature, 

the same ambient temperature may also have a different 

impact on a concrete slab based on its thickness. 

However, surface temperature measurements cannot be 

determined directly in the field for an entire pavement 

section. Therefore, ambient temperatures are suggested as 

a guideline for FWD testing. In addition, the concrete slab 

thicknesses of the 16 JPCP pavements in this study ranged 

from 8 to 11 inches. The suggested guidelines can be valid 

for such slab thicknesses.

Figure 8. Graph. Histogram of the available temperature 

gradients data—SMP JPCP sections.

© 2021 H. Muslim.

A. WF climatic region.

C. WF climatic region. D. WNF climatic region.

B. WNF climatic region.
13

CONCLUSIONS AND RECOMMENDED 

GENERAL FWD MEASUREMENT GUIDELINES

The LTPP SMP study is a valuable data resource that 

can help identify, model, and characterize the effects 

of temporal (i.e., diurnal temperature and seasonal 

temperature/moisture) variations on different pavement 

performance-related parameters (FHWA 2019). Multiple 

measurements related to various aspects of the pavement 

performance and environmental factors available in the 

database made this analysis possible. The study presented 

in this TechBrief used the multiple FWD deflection 

measurements and temperature data to formulate general 

guidelines for FWD measurements on rigid pavements.

FWD measurements are conducted on both rigid and 

flexible pavements. The backcalculated hot-mix asphalt 

layer moduli that are obtained by using deflections 

measured by FWD are typically corrected for temperature. 

However, there is no comparable correction process for 

rigid pavements. Therefore, it is vital to have general 

guidelines for conducting FWD tests on rigid pavements 

to accurately characterize their condition. Based on the 

analysis and the discussion of the results, some general 

guidelines can be formulated and should be adhered 

to, when possible, to conduct FWD testing on concrete 

pavements. The following guidelines can help ascertain 

actual pavement joint conditions and load transferability:

• Avoid conducting FWD deflection measurements 

to calculate a joint’s condition and estimate its load 

transfer capability when temperatures are above 

75 °F because elevated temperatures tend to lock up 

the joints, due to slab expansion, and result in higher 

LTE values.

• Avoid performing FWD deflection measurements 

when temperatures are below 35 °F because lower 

temperatures will also result in LTE values that will 

not truly represent a joint’s conditions owing to its 

resistance to movement.

• Perform FWD measurements before noon, if possible, 

to decrease the chances for the PCC slabs to curl 

down, which can result in locked joints. Thus, the 

determined LTE values will depict the actual load 

transfer capability of the joint.

FUTURE WORK

The TechBrief is based on an analysis of one of the 

FWD deflection-based parameters, LTE, of the rigid 

pavements. A similar study can be conducted for other 

FWD deflection-related parameters, such as PCC layer 

moduli, modulus of subgrade reaction (k-value), and void 

potential under the concrete slab for rigid pavements. The 

results from such future studies can be used to determine 

comprehensive FWD measurement guidelines to help 

engineers better characterize the performance of, and 

measure the actual condition of, rigid pavements

REFERENCES

AASHTO. 1993. AASHTO Guide for Design of Pavement 

Structures, 1993. Washington, DC: American Association 

of State Highway and Transportation Officials.

Choubane, B., and M. Tia. 1992. “Nonlinear Temperature 

Gradient Effect on Maximum Warping Stresses in Rigid 

Pavements.” Transportation Research Record 1370, 

no. 1: 11.

Choubane, B., and M. Tia. 1995. “Analysis and 

Verification of Thermal-Gradient Effects on Concrete 

Pavement.” Journal of Transportation Engineering 121, 

no. 1: 75–81.

Elkins, E. E., and B. Ostrom. 2019. Long-Term 

Pavement Performance Information Management System 

User Guide. Report No. FHWA-RD-03-088 (Final). 

Washington, DC: Federal Highway Administration.

Elkins, E. E., T. Thompson, B. Ostrom, and B. Visintine. 

2018. Long-Term Pavement Performance Information 

Management System User Guide. Report No. FHWARD-03-088 (Update). Washington, DC: Federal 

Highway Administration.

FHWA. 2019. “LTPP InfoPave: Data: Standard Data 

Release” (web page). https://infopave.fhwa.dot.gov/Data/

StandardDataRelease, last accessed June 7, 2022.

Ioannides, A. M., and G. T. Korovesis. 1992. “Analysis 

and Design of Doweled Slab-on-Grade Pavement 

Systems.” Journal of Transportation Engineering 118, 

no. 6: 745–768.

Jeong, J.-H., and D. G. Zollinger. 2005. “Environmental 

Effects on the Behavior of Jointed Plain Concrete 

Pavements.” Journal of Transportation Engineering 131, 

no. 2: 140–148.

McCracken, J. K. 2008. “Seasonal Analysis of the 

Response of Jointed Plain Concrete Pavements to FWD 

and Truck Loads.” Master’s thesis. Pittsburgh, PA: 

University of Pittsburgh.
14

Shoukry, S. N., G. W. William, and M. Y. Riad. 2005. 

Evaluation of Load Transfer Efficiency Measurement. 

Report No. WVU-2002-04. University Park, PA: 

Mid-Atlantic Universities Transportation Center, 

Pennsylvania State University.

Siddique, Z. Q., M. Hossain, and D. Meggers. 

“Temperature and Curling Measurements on Concrete 

Pavement,” in Proceedings of the 2005 Mid-Continent 

Transportation Research Symposium. Ames, IA: Center 

for Transportation Research and Education.

Trujillo, P. B., and M. A. S. Guettero. 2019. “Effect of 

Temperature Gradients on the Behaviour of Jointed Plain 

Concrete Pavements.” Revista IBRACON de Estruturas e 

Materiais 12: 398–407.

Westergaard, H. M. 1926. “Analysis of Stresses in 

Concrete Pavements due to Variations of Temperature.” 

Presented at the Proceedings of the Sixth Annual Meeting 

of the Highway Research Board. Washington, DC: 

Highway Research Board.

Researchers—This study was conducted by Syed Waqar Haider and Hamad Bin Muslim at the Department of Civil 

and Environmental Engineering, Michigan State University with the support of the National Cooperative Highway 

Research Program.

Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution is being 

made to the FHWA divisions and Resource Center.

Availability—This TechBrief may be obtained at https://highways.dot.gov/research.

Key Words—LTPP, SMP, FWD testing, rigid pavements, JPCP, FWD guidelines, seasonal effects.

Notice—This document is disseminated under the sponsorship of the U.S. Department of Transportation (USDOT) 

in the interest of information exchange. The U.S. Government assumes no liability for the use of the information 

contained in this document. The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this TechBrief only because they are considered essential to the objective of 

the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides high quality information 

to serve Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. 

FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous 

quality improvement.

Recommended citation: Federal Highway Administration, 

Effects of Seasonal and Diurnal FWD Measurements 

on LTE of JPCP—LTPP SMP Data 

OCTOBER 2022 (Washington, DC: 2022) https://doi.org/10.21949/1521921.

FHWA-HRT-22-094 

HRDI-30/10-22(WEB)E