TECHBRIEF

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.fhwa.dot.gov/research

Long-Term Pavement 

Performance Program—

Pavement Performance 

Measures and Forecasting 

and the Effects of 

Maintenance and 

Rehabilitation Strategy on

Treatment Effectiveness

FHWA Publication No.: FHWA-HRT-16-046

FHWA Contact: Yan “Jane” Jiang, HRDI-30, (202) 493-3149, 

jane.jiang@dot.gov

This document is a technical summary of the Federal Highway 

Administration Long-Term Pavement Performance Program 

report, Pavement Performance Measures and Forecasting and 

the Effects of Maintenance and Rehabilitation Strategy on 

Treatment Effectiveness (FHWA-HRT-16-047).

Introduction and Objective

This TechBrief presents the methodologies and procedures 

used by the research team in the analyses of the Long-Term 

Pavement Performance (LTPP) data to develop and implement 

pavement performance measures and to analyze treatment 

effectiveness. The LTPP study focused on using data from the 

various LTPP experiments to define pavement performance in 

a way that supports the selection of cost-effective pavement 

treatment strategy and to better estimate pavement treatment effectiveness and the role of pavement treatments in the 

pavement’s lifecycle. This TechBrief includes a description and 

examples of the dual pavement condition rating systems, 

LTPP data analyses results, and application of the analyses to 

datasets from three State transportation departments.

The Long-Term Pavement Performance 

(LTPP) Program is a large research 

project for the study of in-service 

pavements across North America. Its 

goal is to extend the life of highway 

pavements through various designs 

of new and rehabilitated pavement 

structures, using different materials and 

under different loads, environments, 

subgrade soil, and maintenance 

practices. LTPP was established under 

the Strategic Highway Research 

Program and is now managed by 

the Federal Highway Administration.
2

Dual Pavement Condition Rating 

Systems

To address the objectives of this study, 

comprehensive dual pavement condition rating 

systems were developed based on pavement 

conditions and rates of deterioration. An 

accurate pavement condition rating system 

best represents pavement behavior when it is 

based on both current and future pavement 

conditions. The main benefit of including the 

estimation of future conditions is the ability of 

pavement managers to plan, budget, and create 

cost-effective long-term pavement treatment 

strategies to preserve the pavement network. 

Pavement condition ratings based on current 

conditions alone only allow decisions to be 

made for the given data collection cycle and do 

not support lifecycle cost analyses (LCCA).

Balanced and comprehensive dual pavement 

condition rating systems were developed based 

on two types of pavement conditions: functional 

and structural. The functional rating is based 

on ride quality (International Roughness Index 

(IRI)) and safety (rut depth) and is expressed 

by the remaining functional period (RFP). (See 

the section entitled Definitions of RFP and RSP.) 

The structural rating is based on cracking and 

rut depth or faulting and is expressed by the 

remaining structural period (RSP). The RFP and 

RSP could be expanded by the road authority to 

include other pavement conditions and distress 

types such as skid resistance for the RFP, block 

cracking (for the RSP of flexible pavements), or 

corner breaks (for the RSP of rigid pavements). 

In this study, two pavement condition measures 

(IRI and rut depth) were used to calculate 

the RFP and four pavement distress types (alligator, longitudinal, and transverse cracking and rut 

depth or faulting) to calculate the RSP.

Definitions of RFP and RSP

The RFP is defined as the shortest time period 

measured in years from the time of the last 

data collection cycle to the time when a functional condition (e.g., IRI, rut depth, or other) 

reaches its corresponding prespecified threshold value. For a given pavement section 

and when supported by the available data, 

two or more RFP values can be calculated: 

one based on IRI, one on rut depth, one on skid 

resistance, and so forth. The shortest of the RFP 

values is assigned to the pavement section 

in question to flag the section for potential 

treatment actions. It should be noted that the 

measured pavement condition and distress data 

must be retained in the database and used to 

facilitate the selection of treatment types. The 

RFP is depicted in figure 1.

The RSP is defined as the shortest time period 

measured in years from the time of the last 

data collection to the time when a structural 

distress reaches its corresponding prespecified 

threshold value. For a given pavement section 

and when supported by the available data, two 

or more RSP values should be calculated: one 

for transverse, longitudinal, alligator, edge, 

and block cracking and one for either rut depth 

(for flexible pavements) or faulting (for rigid 

pavements). The shortest of the RSP values is 

assigned to the pavement section in question to 

flag the section for potential treatment actions. 

Once again, the measured distress data must 

be retained in the database to facilitate the 

selection of treatment types. The RSP is depicted 

in figure 2.

Rating Scale

Two rating scales could be used for each of 

the RFP and RSP. One scale is based on three 

levels, and the other scale is based on five 

levels. For each scale, the pavement condition states (CSs) can be expressed either in 

descriptive terms or in numeric terms that represent the RFP and the RSP. For example, for 

the three-level scale, the CS of a given 

pavement section may be expressed in one 
3

Figure 1. RFP condition states (CSs) for three- and five-level scales.

Figure 2. RSP CSs for three- and five-level scales.

1 inch/mi = 0.01588 m/km.

1 ft2

/mi = 0.05806 m2/km.
4

CS

RFP Range 

(Year)

RSP Range 

(Year)

Cost per Lane-Mile 

(Lane-Km) $ times 105

Code

Pattern 

or Color Descriptor RFP RSP

1

Horizontal 

solid line 

or red

Poor < 4 < 4 5 (3.1) 10 (6.2)

2

Horizontal 

dashed line 

or yellow

Fair 4 to < 8 4 to < 8 2.2 (1.4) 5.5 (3.4)

3

Diagonal 

crisscross 

line or green

Good > 8 > 8 0.3 (0.2) 1.0 (0.6)

CS

RFP Range 

(Year)

RSP Range 

(Year)

Cost per Lane-Mile 

(Lane-Km) $ times 105

Code

Pattern 

or Color Descriptor RFP RSP

1a

Horizontal 

solid line 

or red

Very poor < 2 < 2 5 (3.1) 10 (6.2)

1b

Vertical 

dashed line 

or pink

Poor 2 to < 4 2 to < 4 3 (1.9) 7 (4.4)

2

Horizontal 

dashed line 

or yellow

Fair 4 to < 8 4 to < 8 1.5 (0.9) 4 (2.5)

3a

Diagonal 

line or light 

green

Good 8 to < 13 8 to < 13 0.5 (0.3) 1.5 (0.9)

3b

Diagonal 

crisscross 

line or green 

Very good > 13 > 13 0.1 (0.1) 0.5 (0.3)

Table 1. Pavement condition rating based on three CSs.

of the following categories as illustrated in this 

list and in table 1:

• CS-1, or red, or poor and RFP or RSP of 

shorter than 4 years.

• CS-2, or yellow, or fair, and RFP or RSP 

between 4 and 8 years.

• CS-3, or green, or good, and RFP or RSP 

longer than or equal to 8 years.

The dual rating systems could be used to select 

treatment categories at the network level. For 

example, preservation treatments should generally be applied to pavement sections with fair 

or better CSs. Heavy preservation treatment, or 

more likely rehabilitation, should generally be 

applied to pavement sections having poor RSP 

CSs. The treatment selection should be verified 

at the project level.

The five-category rating scale included in table 2 

provides finer and additional data analyses.

The three- and five-category rating scales are 

also depicted in figure 1 and figure 2 for RFP 

Table 2. Pavement condition rating based on five CSs.
5

and RSP, respectively. Note that for both 

pavement rating scales, the main reason for 

using the same ranges in years for RFP and 

RSP for each rating category is for ease of 

communication. The CS numbers could be used 

for programming purposes, the color code for 

mapping, the descriptive (poor, fair, and good) 

for communication with the public and legislators, and the RFP and RSP scales for 

planning and for LCCA. The latter could be 

better accomplished if the road agencies used 

their own cost data to assign an average pavement preservation cost per lane mile for each 

RFP range and for each RSP range (similar to the 

conceptual cost included in table 1 and table 2). 

In general, the cost of preserving pavement 

sections based on short RFP is much lower than 

the cost of preservation based on short RSP.

The main advantage of the RFP and RSP is 

that the value of each should decrease 1 year 

for every calendar year. Although the RFP and 

RSP are calculated using nonlinear functions 

to model the pavement condition and distress 

as a function of time (i.e., exponential function 

for IRI, power function for rut depth, and logistic 

(S-shaped curve) for cracking), the resulting 

RFP and RSP are linear functions of time. Each 

should decrease by 1 year for every calendar 

year. Nevertheless, the RFP and RSP can be considered forecasting tools that can be used to 

establish cost-effective strategies that address 

planning, budgeting, and contracting pavement 

preservation activities at the proper time.

One Record Condition State 

Estimate (ORCSE)

As stated earlier, for a given pavement section, 

the RFP and/or the RSP values can be considered 

the forecasting tool to flag pavement sections 

that require attention. The two values are based 

on the measured time-dependent pavement 

condition and distress data. The data are fitted 

to the proper mathematical function, which is 

used to forecast the time at which the pavement 

section in question will reach the threshold 

value. However, for many pavement sections, 

the pavement management database does 

not contain the required minimum three data 

points to model the pavement condition and 

distress as a function of time. This scenario 

is especially true for newly constructed or 

rehabilitated pavement sections. To address 

the problem, the LTPP database was used to 

develop a forecasting system called ORCSE 

based on a single measured data point.(1) In 

the development, the LTPP data from Specific 

Pavement Study (SPS)-1 were analyzed, and 

the RFP an RSP for each test section were 

calculated. The calculated RFP and RSP values 

and one individual measured data point were 

modeled using the Epanechnikov Kernel 

probability density function model. The model 

provided the closest fit to the observed data 

while generating secondary benefits of the 

identification of possible sub-probability groups 

or divergent behaviors within the larger SPS-1 

generalized sample. Table 3 lists CS probabilities 

based on ranges of measured IRI for SPS-1 

test sections before treatment (BT). For older 

SPS-1 sections, the ORCSE results were 

within 5 percent of those using three or more 

data points. Therefore, the ORCSE is a reliable 

method for estimating RFP and RSP while time 

series data are under collection.
6

LTPP Data Analyses

Pavement condition and distress data as well 

as inventory and treatment data were obtained 

from the LTPP Standard Data Release 28. The 

available data for test sections from SPS-1 

through SPS-7, General Pavement Studies 

(GPS)-6, GPS-7, and GPS-9 experiments were 

modeled both before and after the application 

of treatments. For the total of 1,555 test sections 

(see table 4), sufficient data were available and 

were analyzed before treatment (BT) and after 

treatment (AT) in 1,182 instances; sufficient 

data were available and were analyzed BT in only 

1,544 instances, and sufficient data were available and were analyzed AT in only 2,691 instances. 

For each treatment application, the IRI data were 

modeled using an exponential function, rut 

depth data using a power function, and cracking 

data using a logistic (S-shaped) function.

The conclusions and recommendations drawn 

from these analyses are presented in the last 

section of this TechBrief. For each treatment 

application, the RFP and RSP values were 

calculated based on the threshold values. The 

following should be noted:

• The following threshold values are flexible 

and can be adjusted based on the road 

authority goals, constraints, and needs:

• IRI—172 inch/mi (2.7 m/km).

Number 

of Test 

Sections

Number 

of Treated 

Sections

Number of 

Treatment 

Applications

Pavement 

Distress/ 

Condition

Number of Treatment 

Applications Analyzed

BT and AT BT Only AT Only

1,555 1,301 2,674

Cracking 278 463 925

IRI 468 558 911

Rut depth 394 453 747

Faulting 42 70 108

Total 1,182 1,544 2,691

Table 4. Summary of treatments applied to SPS-1 through SPS-7 and GPS-6, -7, and -9.

IRI Range 

(inches/mi)

Probability of a CS or RFP Level for Selected IRI Ranges (percent)

CS 1 or RFP

< 2 years

CS 2 or RFP

2 to < 4 years

CS 3 or RFP

4 to < 8 years

CS 4 or RFP

8 to < 13 years

CS 5 or RFP

≥ 13 years

.25–.50 0 0 0 3 97

.50–.75 0 0 3 14 83

.75–1.00 0 0 4 19 77

1.00–1.25 0 0 9 39 52

1.25–1.50 0 3 19 66 11

1.50–1.75 0 12 49 37 1

1.75–2.00 3 28 59 10 0

2.00–2.25 13 61 22 3 0

2.25–2.50 55 36 6 3 0

2.50–2.70 88 8 1 2 0

Table 3. ORCSE model table example from LTPP SPS-1 BT evaluation.

1 inch/mi = 0.02 m/km.
7

• Rut depth—0.5 inch (12.7 mm).

• Alligator cracking—1,267 ft2/0.1 mi 

(73 m2

/0.1 km).

• Longitudinal cracking—1,056 ft/0.1 mi 

(201 m/0.1 km).

• Transverse cracking (hot-mix asphalt 

(HMA))—350 ft/0.1 mi (67 m/0.1 km).

• Transverse cracking (jointed concrete pavement (JCP))—264 ft/0.1 mi 

(50 m/0.1 km). 

• Faulting-average—0.25 inch over 0.1 mi

(3.97 mm over 0.1 km).

• These threshold values cannot and should 

not be used to select certain pavement 

preservation actions such as thin or thick 

overlay or mill-and-fill treatments. The 

threshold values indicate the minimum 

acceptable level of service or pavement 

condition. The resulting RFP and RSP are 

mainly used to flag pavement sections in 

need of preservation. The flagging level 

could be set at RFP and/or RSP values of 4 

or 7 years or whatever value is perceived 

by the road owners based on their needs 

and constraints.

• The selection of cost-effective pavement 

preservation treatments should be based 

on the measured pavement condition 

and distress data and on the causes of 

pavement deterioration.

Application of Methodology to State Data

Similar analyses to those performed on the 

LTPP data were performed on pavement 

condition and distress data measured by three 

State transportation departments (Colorado, 

Louisiana, and Washington) along various 

pavement projects of their respective pavement 

networks. The data collected and stored by the 

State transportation departments are similar 

to the LTPP data with few exceptions. These 

data include the pavement segment length, 

definition of some pavement distresses and 

units of measurement, pavement structure or 

cross-section, and other inventory data. The 

three State transportation departments collect 

and store data for each 0.1-mi (0.16-km)-long 

pavement segment along their respective 

networks. For each pavement project and for 

each data collection cycle, the number of data 

points available in the database is the same as 

the number of consecutive 0.1-mi (0.16-km)-

long pavement segments along the project. The 

analyzed pavement projects were subjected to 

various treatments, including single chip seal, 

thin and thick HMA overlays, and thin and thick 

mill-and-fill treatments.

Nevertheless, results of the analyses indicate 

the following:

• The newly developed dual pavement 

performance rating systems are applicable 

to the State and the LTPP data.

• The treatment benefits calculated using the 

LTPP data are almost equal to the treatment 

benefits calculated using the State data.

• The variability of the measured IRI, rut 

depth, and cracking data along a given 

pavement project is similar to the variability in the same data of comparable 

LTPP test sections.

The important implication of the findings is that 

the analysis methodologies described in this 

study can be used by the various State transportation departments. Furthermore, the RFP and 

RSP concept and the CSs apply equally to the 

State and the LTPP data.

Finally, the research team strongly recommends 

that treatment benefit benchmarks obtained 

from the analyses of the LTPP data be adopted 

and used by the American Association of State 
8

Highway and Transportation Officials (AASHTO) 

and the State transportation departments. This 

would standardize the designations of the treatment benefits prior to the LCCA.

Deflection Data Analyses

The RFP and RSP algorithms are primarily 

based on the measured time-series pavement 

surface condition and distress data and their 

corresponding threshold values. Hence, the 

distress (such as cracking) must be visible from 

the pavement surface to be counted. During the 

development of the RFP and RSP concepts, it 

was envisioned that the pavement deflection 

data could be used to indicate impending 

surface distress and be a part of the RSP 

algorithm. Such algorithms would empower 

State transportation departments to take 

corrective actions prior to the manifestation of 

surface defects. To incorporate deflection into 

the RSP algorithm, a deflection threshold value 

must be developed for each pavement section. 

To investigate the potential for the development 

of deflection threshold values, the measured 

falling weight deflectometer (FWD) deflection 

data of various LTPP seasonal monitoring 

program (SMP) and other test sections were 

analyzed.

The main purpose of the analyses of the 

deflection data is to identify relationships, if any, 

between the measured pavement deflection 

and the measured pavement condition data 

to determine whether the deflection data can 

be used to estimate the optimum time for 

pavement preservation. To conduct the analyses, 

the measured pavement surface deflections 

of some flexible pavement test sections were 

examined. Figure 3 shows the impacts of the 

measured pavement surface temperature on 

the pavement surface deflection measured at 

deflection sensors 1, 2, 4, and 7 (0, 8, 18, and 

60 inches (0, 203, 457, and 1,524 mm) from the 

load) of the FWD. As was expected, the data 

indicate that the effects of temperature decrease 

with increasing distance from the center of the 

FWD load plate. The measured deflections were 

then adjusted to the standard temperature of 70 

oF (21 oC) using existing temperature adjustment 

models, such as the Asphalt Institute (AI) and 

BELLS models.(2,3) None of the SMP site results 

agreed with the measured deflection data at 

the standard temperature of 70 oF (21 oC). 

Therefore, a global temperature adjustment 

procedure was developed using the LTPP 

measured deflection data along with various 

SMP test sections. The new procedure is 

applicable to all deflection sensors and in all 

climatic regions.

The AI method and the new procedure were 

then used to adjust the measured deflection 

data over time to the standard temperature of 

70 oF (21 oC). Figure 4 depicts the percent error 

between the temperature-adjusted deflection 

data and the measured deflection data at 70 oF 

(21 oC) at the same SMP test site. It can be seen 

that the new procedure has much smaller errors 

than the AI procedure.

After adjusting the measured deflection data 

to 70 oF (21 oC), the adjusted data of one test 

section were then plotted against the pavement 

distress data that were measured at the 

same SMP test section. It was found that the 

measured and temperature-adjusted deflection 

did not correlate with the measured cracking 

data. In fact, the deflection data did not show 

any consistent pattern against time. Therefore, 

the data cannot be used to develop a deflection 

threshold value and cannot be included in the 

RSP algorithm. However, the deflection data can 

be used to estimate the moduli of the pavement 

layers and to design pavement treatments.
9

Figure 3. Peak measured pavement deflection at sensors 1, 2, 4, and 7 versus pavement surface temperature for LTPP test 

section 010101, F3.

Figure 4. Percent errors of the temperature-adjusted deflection data using the new procedure and the AI procedure.

oC = (5/9)(oF–32o).

1 mil = 25.4 micron.

oC = (5/9)(oF–32o).
10

Conclusions

Based on the results of the analyses, conclusions 

and recommendations were drawn that cover a 

range of study-related topics.

Pavement Performance Measures

The researchers drew the following conclusions 

related to pavement performance measures:

• The pavement cracking data are 

typically collected and stored based on 

low, medium, and high severity levels. 

Excessive variability from one year to the 

next creates a problem in analyzing per 

severity level, but analyses of the cracking 

data based on the sum of all severity 

levels has been proven to overcome the 

problem.

• For pavement projects that used the 

same treatment type, treatment transition 

matrices (T2

Ms) can be developed to 

display the distribution of the pavement 

conditions along the project BT and AT. 

The data in the T2

Ms can be and were 

used to estimate the benefits of the 

various treatments.

• Pavement condition rating should be 

based on current conditions and distresses as well as the pavement’s rates of 

deterioration.

• The three- and five-level dual pavement 

condition rating systems developed in this 

study are useful and were equally applied 

to both State and LTPP data. The systems 

are flexible and can be easily tailored to 

fit the needs and constraints of any road 

agency.

• The estimated average cost of pavement 

preservation for each level of the dual 

pavement rating system can be used in the 

LCCAs and in strategy optimization.

• Threshold values were provided for 

calculation of the RFP and RSP. The 

values are based on minimum level of 

service to the user (functional) and loss of 

structural integrity (structural).

Flexible Pavements

Concerning flexible pavements, the researchers 

drew the following conclusions:

• Conditions in wet-freeze (WF) regions have 

significant adverse impacts on pavement 

performance in terms of IRI, rut depth, and 

cracking.

• Drainable bases decrease the impacts of 

the conditions in WF regions on pavement 

performance. The improvement in the 

pavement performance from use of drainable bases is slightly better than that from 

increasing the asphalt concrete (AC) layer 

thickness from 4 to 7 inches (102 to 178 mm).

 Therefore, the use of drainable bases in the 

WF region is cost effective. 

• The inclusion of drainable bases in the dry 

freeze (DF) and dry-no-freeze (DNF) regions 

does not affect pavement performance in 

terms of RFP or RSP. This was expected 

because the volume and frequency of 

available water are low. Furthermore, most 

rainfall takes place over short periods of 

time where most water runs off the surface 

and does not penetrate the pavement 

layers.

• Increasing the thickness of the AC layer 

from 4 to 7 inches (102 to 178 mm) 

increases the frost protection of the lower 

layers and decreases the impact of the 

conditions in the WF region on pavement 

performance. However, this option is not 

cost effective.

• The conditions in the wet-no-freeze (WNF), 

DF, and DNF regions do not affect the 
11

pavement performance relative to rutting 

potential and IRI.

• The conditions in the DF region have more 

adverse effects on cracking potential than 

those in the DNF region mainly because 

of higher oxidation (aging) potential of the 

AC layer in the DF region.

• The thin overlay treatment improves the 

pavement performance of the SPS-3 test 

sections in terms of IRI and rut depth in 

all climatic regions except the DNF region. 

This could be related to construction issues 

and, perhaps, to relatively high solar 

radiation (accelerated oxidation/aging) in 

the DNF region.

• In general, the thin overlay treatment does 

not improve the pavement performance of 

the SPS-3 test sections in terms of alligator, 

longitudinal, and transverse cracking. 

This is mainly because of the high rate 

of reflective cracking. Immediately after 

treatment, all cracks are hidden by the 

thin overlay. However, within a few years, 

most cracks are reflected through the 

overlay, which implies relatively high rate 

of deterioration and therefore short RSP. 

The exception is in the DNF region where 

the two test sections showed an increase 

of 12 years in the average RSP relative 

to the one control section. This oddity is 

mainly related to the limited number of 

test sections (i.e., the conclusion is not 

reliable because of the limited number of 

test sections and control sections).

• The slurry seal treatment improves the 

pavement performance of the SPS-3 test 

sections in terms of IRI and rut depth but 

does not have much impact on alligator, 

longitudinal, and transverse cracking.

• Crack sealing appears to improve the 

pavement performance of the SPS-3 test 

sections in terms of rutting. However, it did 

not improve the pavement performance 

relative to cracking.

• Aggregate seal coats appear to improve 

the pavement performance of the SPS-3 

test sections in all climatic regions in terms 

of IRI, rut depth, and cracking.

• In general, the worse the pavement 

conditions are BT, the shorter the benefits 

of treatments in terms of the RFP and/or 

RSP values.

• On average, the impact of 2- and 4-inch 

(50.8- and 101.6-mm) virgin or recycled AC 

overlays on pavement performance of the 

SPS-5 test sections is almost the same.

• The 2-inch (50.8-mm)-thick AC overlay 

(virgin or recycled mix) does not provide 

a long-term remediation of transverse 

cracking. The cracks in the lower pavement 

structure typically reflect through the 

overlay in a few years.

• On average, the change in the structural 

period of a flexible pavement structure as 

a result of 2-inch (50.8-mm) AC overlay in 

terms of alligator cracking is slightly less 

than the 4-inch (101.6-mm) overlay.

• In each climatic region, the impacts of the 

thin and thick overlay or thin and thick milland-fill treatments on IRI and rut depths 

are almost the same. This was expected 

because good quality construction can 

decrease the pavement surface roughness 

substantially and remove ruts regardless 

of the overlay thickness.

ORCSE Method

The researchers concluded the following:

• The ORCSE method was successful in 

predicting the CSs of the test sections 

based on RFP and RSP. For older test 
12

sections, the average estimation error for 

the most critical CSs is less than 1 percent. 

• The ORCSE method provides significant 

potential benefits to local roadway owners 

as well as to State transportation department managers when only two or fewer 

data points are available.

Rigid and Composite Pavements

Concerning rigid and composite pavements, the 

researchers concluded the following:

• On average, the majority of the SPS-2 

test sections located in the WNF region 

performed worse in terms of longitudinal cracking than those in the DNF 

region. This is mainly owing to the impact 

of excessive moisture on pavement 

performance.

• The conditions in the WF region have a 

more damaging impact on the performance of the SPS-4 test sections in terms 

of transverse cracking than test sections 

located in the WNF, DF, and DNF regions. 

This was expected owing to the combined 

effects of subfreezing temperatures and 

moisture.

• On average, in terms of IRI, joint and crack 

sealing treatment has positive impact on 

the performance of the SPS-4 test sections 

located in the WNF region, no impact in the 

WF region, and negative impact in the DF 

and DNF regions.

• Joint and crack sealing is effective in the 

WF region and not effective in the other 

three climatic regions. Joint undersealing 

is not effective in any region.

• The performance of treated SPS-6 test 

sections in terms of IRI is independent of 

the climatic region and pavement type. It 

is also independent of treatment type in 

terms of rut depth.

• It is highly likely that the alligator cracking 

data in the SPS-6 database represent an 

advanced form of top-down fatigue cracking: (i.e., the top-down cracks are fatigue 

cracks that initiate at the pavement surface 

and propagate downward over time). The 

short transverse and longitudinal cracks 

resemble the traditional bottom-up alligator cracking pattern.

• The performance of the test sections in 

terms of longitudinal cracking was worse 

after subjecting the section to any of the 

seven analyzed treatment types.

• Minimum and maximum pavement 

restoration with no AC overlay treatments 

do not improve the performance of the 

jointed reinforced concrete pavement test 

sections.

• The IRI-based performance of the 

treated continuously reinforced concrete 

pavements (CRCP) (SPS-7) test sections is 

independent of the eight treatment types 

and their presence in the WF and WNF 

climatic regions.

• The performance of the jointed plane 

concrete pavement (JPCP) test sections 

subjected to 3-inch (76.2-mm) concrete 

overlay with milling (703) or with shot 

blasting (704) treatments is lower than the 

performance of the other JPCP test sections 

subjected to the other six treatments. None 
13

of the eight applied treatments are effective 

in treating transverse cracking problems of 

the CRCP test sections.

Deflection

In the area of deflection, the researchers drew 

the following conclusions:

• The LTPP deflection data collected for the 

SMP test sections do not support the use of 

the AI temperature adjustment procedure 

of the measured deflection data. 

• The newly developed global flexible 

pavement deflection temperature 

adjustment algorithm is applicable to all 

deflection sensors and all climatic regions.

• The LTPP deflection data collected for 

the SMP test sections do not correlate to 

the measured pavement condition and 

distress data. Therefore, the data do not 

support their inclusion in the RFP and RSP 

algorithms.

• The LTPP deflection and load transfer 

efficiency data collected for the SPS-2 

test sections within Arizona, Colorado, 

Delaware, and Michigan do not support the 

modeling of the data as a function of time.

State Data

The State data indicate the following:

• The weighted average benefits of each of 

five treatment types (thin overlay (≤ 2.5 inch 

(63.5 mm)), thick overlay (> 2.5 inch 

(63.5 mm)), thin mill and fill (≤ 2.5 inch 

(63.5 mm)), thick mill and fill (> 2.5 inch 

(63.5 mm)), and single chip seal) relative 

to each pavement condition and distress 

type obtained from the analyses of the 

LTPP data are similar to the benefits 

obtained from the three State transportation departments’ data.

• The treatment benefits provided in this 

report using the LTPP data can be used 

as benchmark values for the national 

practice. This data may be used by State 

transportation departments to do the 

following:

• Gauge the effectiveness of their current practices using similar analyses.

• Conduct LCCA of various treatment 

alternatives to optimize the pavement 

rehabilitation and treatment strategy 

at the network level.

• The methodologies described in this report 

for the analyses of the LTPP pavement 

condition and distress data also apply to 

the State data.

• The three- (poor, fair, and good) and fivelevel (very poor, poor, fair, good, and very 

good) pavement rating systems developed 

in this study, based on the time series 

pavement condition and distress, are 

equally applicable to the LTPP and the 

State data. 

• The average variability in the pavement 

condition and distress data of the LTPP test 

sections is almost the same as the average 

variability of the State measured data. 

• The percent of the LTPP test sections that 

were excluded from the analyses because 

of inadequate numbers of data points or 

improving pavement condition and/or 
14

distress over time without the application 

of treatments is similar to the percent 

of the 0.1-mi (0.16-km)-long pavement 

segments of a given pavement project that 

was excluded from the analyses for the 

same reasons.

Recommendations

Performance Measures

Based on the results of the LTPP and State data 

analyses and the conclusions listed above, the 

following actions are strongly recommended:

• Use the sum of crack lengths or crack 

areas of all severity levels to model the 

pavement condition and distress data as 

a function of time.

• Base accurate pavement planning and 

management decisions on the pavement 

conditions and rates of deterioration.

• Have FHWA adopt the three- and/or 

five-level rating systems and have them 

submitted to AASHTO for approval.

• Adopt either the threshold values used in 

this study to estimate the RFP and RSP of 

the various pavement sections or similar 

ones developed by the road agencies.

• Have each road agency develop the 

average cost of pavement preservation for 

each RFP and RSP range of the dual 

pavement rating systems using their own 

cost record.

• Perform LCCA at the project level and 

strategy optimization at the network level 

to improve the overall cost effectiveness 

of the pavement management application.

• Have highway agencies adopt and use 

the T2

M procedure to assess treatment 

effectiveness and to select the optimum 

treatment time.

• Use the dual pavement rating systems 

in future analyses and assessment of the 

benefits of pavement rehabilitation and/or 

maintenance treatments.

Flexible Pavements

Concerning flexible pavements, the following 

actions are strongly recommended:

• Drainable bases should be constructed 

to enhance the performance of pavement 

sections located in the WF region.

• For future studies, the control or linked test 

sections should be selected to border the 

regular test sections in question, and their 

history should be included in the database. This would eliminate unnecessary 

variability.

• The pavement condition and distress data 

should be measured BT and AT. The quality 

control data for project acceptance should 

be included in the pavement management 

system database.

• The frequency of pavement condition 

and distress data collection should be 

considered a function of treatment type. 

Treatments having short treatment life 

should be surveyed more often than longlife treatments.

ORCSE Method

The researchers recommend that the ORCSE 

method be expanded to other pavement conditions and distresses and applied to a wider 

range of LTPP and State transportation department data to further verify its successful 

prediction of pavement CSs.
15

Deflection

In the area of deflection, the researchers 

recommend the following:

• The new global flexible pavement deflection temperature adjustment algorithm 

should be adopted and used to adjust the 

measured pavement deflection to a standard temperature.

• The estimation of the AC modulus at the 

standard temperature of 70 oF (21 oC) could 

be accomplished using one of the following 

two procedures:

• Adjust the measured deflection 

data to 70 oF (21 oC) using the newly 

developed global model and then 

backcalculate the modulus.

• Conduct a minimum of three FWD 

tests and measure the pavement 

deflection at three temperatures. 

Backcalculate the AC modulus value 

for each of the three FWD tests. Plot 

the modulus as a function of the 

measured pavement temperature 

and estimate the AC modulus at 70 oF 

(21 oC) from the graph.

• For new LTPP experiments or road tests, 

FWD tests should be conducted BT and AT.

State Data

Concerning the State data, the following are 

recommended:

• FHWA, AASHTO, and the State 

transportation departments should adopt 

the dual pavement condition rating 

systems to unify the analyses of pavement 

performance nationwide.

• The benchmark benefit values of the five 

treatment types included in this study 

should be expanded to include additional 

pavement treatments.

Future Studies

Future studies should address the following 

recommendations:

• Initiate studies to produce a national catalog of the service life of various treatment 

types as a function of pretreatment pavement conditions and distress. The studies 

should be based mainly on the LTPP data 

and applied to some State data.

• Initiate studies to establish automated 

data collection processes, quality control 

procedures, and data storage to minimize 

the impact of subjective factors and 

human error on pavement conditions and 

distress. 

• Initiate studies to scrutinize the newly 

developed self-powered wireless Pico 

sensors (3.9-13 inches (9.9-12 mm)) that can 

be embedded in pavement and transportation infrastructures to measure their 

performance in terms of cracking and 

induced stresses and strains.

• Eliminate the need for traditional data 

collection by exploring the accuracy of the 

newly developed chemical sensors that can 

be included in concrete and asphalt mixes 

to measure future pavement conditions 

and distresses.

• Develop short course materials that include 

examples to train engineers and staff of 

various State transportation departments 

to use the MATLAB® computer program 

for the analyses of pavement condition and 

distress and to emphasize the benefits of 

the LTPP Program.
16

Researchers—This study was performed by Michigan State University Department of Civil & 

Environmental Engineering in East Lansing, MI, and NTH Consultants, Ltd., in Lansing, MI.

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

distribution is being made to the Divisions and Resource Center.

Availability—This TechBrief may be obtained from 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 performance; pavement rehabilitation; LTPP data; data analysis; good, fair, and 

poor; remaining service life; remaining functional period; remaining structural period. 

Notice—This document is disseminated under the sponsorship of the U.S. Department of 

Transportation 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 report only because 

they are considered essential to the objective of the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides highquality information to serve the Government, industry, and public in a manner that promotes public 

understanding. Standards and policies are used to ensure and to maximize the quality, objectivity, 

utility, and integrity of its information. Periodically, FHWA reviews quality issues and adjusts its 

programs and processes to ensure continuous quality improvement.

AUGUST 2016 FHWA-HRT-16-046

HRDI-30/08-16(500)E

References

1. FHWA. (2014). LTPP Pavement Performance 

Database Release Notes, Standard Data 

Release 28, Federal Highway Administration, Washington, DC, obtained from: 

https://infopave.fhwa.dot.gov/Data/

StandardDataRelease, last accessed 

August 1, 2015.

2. Asphalt Institute. (1983). Asphalt Overlays 

for Highway and Street Rehabilitation, 

Manual Series No. 17 (MS-17), Lexington, KY.

3. Lukanen, E.O., Stubstad, R., and Briggs, 

R. (2000). Temperature Predictions and 

Adjustment Factors for Asphalt Pavement, 

Report No. FHWA-RD-98-085, Federal 

Highway Administration, Washington, DC.