Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Reformulated Pavement Remaining

Service Life Framework

Publication No. FHWA-HRT-13-038 november 2013
FOREWORD

Many important decisions are necessary in order to successfully provide and manage a pavement 

network. At the heart of this process is the prediction of needed future construction events. One 

approach to providing a single numeric on the condition of a pavement network is the use of 

pavement remaining service life (RSL). However, many issues exist with the current RSL 

terminology and resulting numeric that complicate proper interpretation, interagency data 

exchange, and use. A major source of uncertainty in the current RSL definition is the use of the 

term “life” to represent multiple points in the pavement construction history. The recommended 

path to consistency involves adopting terminology of time remaining until a defined construction 

treatment is required (i.e., RSL is replaced by remaining service interval (RSI)). The term “RSI”

has the ability to unify the outcome of different approaches to determine needs by focusing on 

when and what treatments are needed and the service interruption created. This report presents 

the framework for replacing the current RSL terminology with one based on more exact 

construction event terms. It provides detailed information on the research performed concerning

remaining pavement life. It explores many issues that exist with the current RSL terminology 

that complicate proper interpretation, interagency data exchange, and use. While this report

focuses on pavements, it is also applicable to other types of transportation infrastructure. A 

companion document provides step-by-step guidelines for implementing the RSI terminology.(1)

This report is intended for use by pavement managers and pavement investment decisionmakers 

across the United States.

Jorge E. Pagán-Ortiz

Director, Office of Infrastructure

Research and Development

Notice

This document is distributed under the sponsorship of the U.S. Department of Transportation in 

the interest of information exchange. The U.S. Government assumes no liability for its contents 

or use thereof. This report does not constitute a standard, specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trade and manufacturers’ 

names appear in this report only because they are considered essential to the object 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 the 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvements.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-13-038

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Reformulated Pavement Remaining Service Life Framework

5. Report Date

November 2013

6. Performing Organization Code

7. Author(s)

Gary E. Elkins, Travis M. Thompson, Jonathan L. Groeger, 

Beth Visintine, and Gonzalo R. Rada

8. Performing Organization Report No.

9. Performing Organization Name and Address

AMEC Environment and Infrastructure, Inc.

12000 Indian Creek Court, Suite F

Beltsville, MD 20705-1242

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-08-C-00033

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Draft Report, October 2009–April 2012

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Representative (COR) was Nadarajah Sivaneswaran, HRDI-20.

16. Abstract

Many important decisions are necessary in order to effectively provide and manage a pavement network. At the heart 

of this process is the prediction of needed future construction events. One approach to providing a single numeric on 

the condition of a pavement network is the use of pavement remaining service life (RSL). However, many issues exist 

with the current RSL terminology and resulting numeric that complicate proper interpretation, interagency data 

exchange, and use. A major source of uncertainty in the current RSL definition is the use of the term “life” to 

represent multiple points in the pavement construction history. The recommended path to consistency involves 

adopting terminology of time remaining until a defined construction treatment is required (i.e., RSL is replaced by 

remaining service interval (RSI)). The term “RSI” has the ability to unify the outcome of different approaches to 

determine needs by focusing on when and what treatments are needed and the service interruption created. This report 

presents the framework for replacing the current RSL terminology with one based on more exact construction event 

terms. A companion document provides step-by-step guidelines for implementing the RSI terminology.(1)

17. Key Words

Pavement remaining service life, Pavement remaining life, 

Pavement remaining service interval, Pavement 

construction events, Pavement construction triggers, 

Pavement threshold limits, Pavement performance curves, 

Pavement data collection, Pavement strategy selection

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

80

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION................................................................................................ 3

INTRODUCTION................................................................................................................... 3

PAVEMENT BUSINESS DECISIONS ................................................................................ 3

ROLE OF RSL IN PAVEMENT BUSINESS DECISIONS ............................................... 4

ISSUES WITH CURRENT RSL TERMINOLOGY .......................................................... 6

FRAMEWORK PRESENTED IN THIS REPORT............................................................ 7

CHAPTER 2. REFOCUS OF PAVEMENT REMAINING LIFE VOCABULARY ............. 9

REFORMULATING RSL TERMINOLOGY..................................................................... 9

FUNDAMENTAL PAVEMENT PLANNING ELEMENTS............................................ 10

CONSTRUCTION EVENT TERMINOLOGY................................................................. 10

CHAPTER 3. FUTURE CONSTRUCTION NEEDS ANALYSIS FRAMEWORK ........... 13

INTRODUCTION................................................................................................................. 13

CONSTRUCTION TRIGGERS.......................................................................................... 14

THRESHOLD LIMITS........................................................................................................ 14

PERFORMANCE CURVES................................................................................................ 14

COLLECTION OF INPUTS ............................................................................................... 15

STRATEGY SELECTION .................................................................................................. 15

ASSESSMENT AND UPDATE........................................................................................... 15

CHAPTER 4. CONSTRUCTION TRIGGERS ....................................................................... 17

INTRODUCTION................................................................................................................. 17

LEVEL OF SERVICE.......................................................................................................... 18

PAVEMENT SURFACE DISTRESS ................................................................................. 18

STRUCTURAL CONSIDERATIONS ............................................................................... 19

SAFETY................................................................................................................................. 20

AGENCY TIME-BASED RULES ...................................................................................... 20

TRAFFIC CAPACITY......................................................................................................... 20

CHAPTER 5. THRESHOLD LIMITS ..................................................................................... 21

INTRODUCTION................................................................................................................. 21

SUBJECTIVE THRESHOLD LIMITS.............................................................................. 21

ENGINEERING CONSIDERATIONS .............................................................................. 23

EMPIRICAL METHODS.................................................................................................... 24

ECONOMIC ANALYSIS .................................................................................................... 25

COMBINATION OF APPROACHES ............................................................................... 26

CHAPTER 6. PERFORMANCE CURVES............................................................................. 29

INTRODUCTION................................................................................................................. 29

MODELS BASED ON DESIGN EQUATIONS ................................................................ 29

EMPIRICAL MODELS....................................................................................................... 31

AGENCY TIME-BASED RULES ...................................................................................... 32

CHAPTER 7. COLLECTION OF INPUTS............................................................................. 33

INTRODUCTION................................................................................................................. 33
iv

PAVEMENT ROUGHNESS................................................................................................ 33

PAVEMENT DISTRESS ..................................................................................................... 33

PAVEMENT STRUCTURAL RESPONSE....................................................................... 34

TRAFFIC LOADS................................................................................................................ 34

CLIMATE ............................................................................................................................. 35

MISSING DATA................................................................................................................... 35

MEASUREMENT VARIABILITY .................................................................................... 35

SAMPLING INTERVALS AND FREQUENCY............................................................... 36

CHAPTER 8. STRATEGY SELECTION................................................................................ 37

INTRODUCTION................................................................................................................. 37

NETWORK ANALYSIS...................................................................................................... 37

PROJECT ANALYSIS......................................................................................................... 38

CHAPTER 9. ASSESSMENT AND UPDATE ........................................................................ 39

INTRODUCTION................................................................................................................. 39

ASSESSMENTS.................................................................................................................... 39

UPDATES.............................................................................................................................. 39

CHAPTER 10. SUMMARY....................................................................................................... 41

APPENDIX A. PAVEMENT DESIGN AND MANAGEMENT CONCEPTS ..................... 43

PAVEMENT DESIGN BASICS.......................................................................................... 43

STAGED CONSTRUCTION AND PERPETUAL PAVEMENTS ................................. 44

MULTIPLE DISTRESS-BASED PAVEMENT DESIGN................................................ 46

REHABILITATION AND RECONSTRUCTION DESIGN............................................ 46

PAVEMENT ECONOMICS................................................................................................ 47

SUMMARY ........................................................................................................................... 49

APPENDIX B. PAVEMENT RSL PREDICTION MODELS................................................ 51

INTRODUCTION................................................................................................................. 51

CATEGORIZATION OF RSL MODELS ......................................................................... 51

LIFE TABLE SURVIVOR CURVES................................................................................. 51

KAPLAN-MEIER SURVIVAL ANALYSIS...................................................................... 52

FAILURE TIME THEORY................................................................................................. 52

COX PH MODEL................................................................................................................. 52

1986 AND 1993 AASHTO PAVEMENT DESIGN GUIDES ........................................... 53

HPMS/HERS MODELS....................................................................................................... 53

OTHER CONTEMPORARY PAVEMENT RSL MODELS........................................... 56

NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM (NCHRP) 

PROJECT 08-71.................................................................................................................... 57

FRICTION............................................................................................................................. 58

NOISE.................................................................................................................................... 59

CONCEPTS FROM OUTSIDE THE PAVEMENT INDUSTRY ................................... 59

LITERATURE REVIEW OBSERVATIONS.................................................................... 64

REFERENCES............................................................................................................................ 67

BIBLIOGRAPHY....................................................................................................................... 71
v

LIST OF FIGURES

Figure 1. Flowchart. Future pavement construction needs process.............................................. 13

Figure 2. Graph. Conceptual relationship between agency repair costs as a function of 

pavement condition....................................................................................................................... 26

Figure 3. Graph. Basic concept of modern pavement design ....................................................... 43

Figure 4. Graph. Illustrated service histories of trial pavement designs incorporating future 

overlays......................................................................................................................................... 44

Figure 5. Graph. Staged pavement construction design concept.................................................. 45

Figure 6. Graph. Perpetual pavement design concept based on construction of a pavement 

where distresses occur in the pavement surface layer .................................................................. 45

Figure 7. Graph. Multiple distress-based pavement design where one of the distresses 

reaches a maximum threshold limit.............................................................................................. 46

Figure 8. Graph. Three treatment zones as a function of pavement condition ............................. 47

Figure 9. Graph. Conceptual tradeoffs among pavement resistive capacity, construction 

costs, and maintenance, repair, and restoration costs ................................................................... 48

Figure 10. Graph. Concept of increasing repair cost as a function of pavement deterioration .... 49

Figure 11. Graph. Classical bathtub curve of component failure rate versus time....................... 61

LIST OF TABLES

Table 1. Role of RSL models in levels and types of pavement management business 

decisions.......................................................................................................................................... 5

Table 2. PSR threshold values used in the HPMS analytical process for minimum 

tolerable conditions for overlay and reconstruction...................................................................... 54
vi

LIST OF ABBREVIATIONS

AASHO American Association of State Highway Officials

AASHTO American Association of State Highway and Transportation Officials 

AC Asphalt concrete

ADT Average daily traffic

ESAL Equivalent single-axle load

FN Friction number

FHWA Federal Highway Administration

FWD Falling weight deflectometer

HERS Highway Economic Requirements System

HMA Hot mix asphalt

HPMS Highway Performance Monitoring System

IRI International Roughness Index

JPCP Jointed plain concrete pavement 

LCC Life-cycle cost

LCCA Life-cycle cost analysis

LTPP Long-Term Pavement Performance

MEPDG Mechanistic-Empirical Pavement Design Guide

M&R Maintenance and rehabilitation

MTBF Mean time between failures

NAPCOM National Pavement Cost Model

NCHRP National Cooperative Highway Research Program

NCDC National Climate Data Center

NHS National Highway System

ODOT Ohio Department of Transportation

PCC Portland cement concrete

PCI Pavement Condition Index

PDF Probability density function

PH Proportional hazard

PHT Pavement health track

PMS Pavement management system

PSI Present Serviceability Index
vii

PSR Present serviceability rating

RSI Remaining service interval

RSL Remaining service life

SHA State highway agency

SI Serviceability Index

SN Structural number

1

EXECUTIVE SUMMARY

The remaining service life (RSL) concept has been around for decades and is well entrenched in 

the pavement community. It is used at all levels of the pavement management decision process 

to plan for future field construction events. However, there is no single, clear, widely accepted 

definition of RSL. Moreover, there is a great deal of uncertainty associated with the definition, 

especially with the use of the term “life” to represent different points in a pavement’s 

construction history. In addition, “life” is interpreted differently by stakeholders. 

To overcome the RSL shortcomings, this framework introduces terminology that removes the 

word “life” from the lexicon since it is the basis for confusion. Instead, the new terminology, 

known as the remaining service interval (RSI) introduces the concept of time remaining until a 

defined construction event is required. Pavements are comprised of interrelated structural parts 

that can be maintained, preserved, restored, rehabilitated, or reconstructed to serve the intended 

transportation needs.

The RSI concept does not provide an alternative to assessing the health of the network or making 

decisions about where to spend the available funds. It simply provides a clear terminology and a 

logical process that will create a consistent construction event-based terminology and 

understanding (i.e., types of construction events and the timing of those events within the 

concept of life-cycle cost (LCC), risk analyses, and other prioritization approaches based on 

streams of future construction events and benefits to facility users). 

3

CHAPTER 1. INTRODUCTION

INTRODUCTION

The prediction of future pavement preservation, repair, rehabilitation, and reconstruction 

requirements is the fundamental basis of engineering design and management of pavement 

structures. Pavement design methods are based on the provision of a pavement structure 

predicted to remain in acceptable condition under anticipated traffic loads and environmental 

conditions for a specified number of years. After construction, planning the timing of 

maintenance, preservation, resurfacing, rehabilitation, and reconstruction activities that take into 

account the material and structural as-constructed pavement characteristics become important. 

Life terminology has been developed and used in various ways to describe the expected period 

of time it will take for a pavement to reach an unacceptable condition state. 

RSL is the time from the present (i.e., today) to when a pavement reaches an unacceptable 

condition requiring construction intervention. While some make the distinction that remaining 

life is the time until major rehabilitation and RSL is the time until a service threshold is reached, 

both types of events require some type of construction treatment to correct. 

This report provides a framework to replace current pavement life terminology with one based 

on more exact construction event terms. The remainder of this chapter discusses pavement 

business decisions and the role that current RSL terminology serves in these decisions. This 

is followed by a discussion of issues with current RSL terminology. 

PAVEMENT BUSINESS DECISIONS

Many decisions need to be made to effectively provide and manage a pavement network. A 

common method to classify these decisions into groups with common characteristics is through 

the notion of hierarchal levels within the business management process. The pavement 

management business processes impacted by these decisions are most often described as 

network- and project-level decisions. 

The purposes and goals of network-level pavement management are normally related to the 

budget process.(2) Decisions at this level are made by higher-level management positions within 

an organization with resource allocation authority. 

Network-level decisions have many strata starting at the local level and progressing to the 

national level. In local municipal agencies, network-level decisions start at the city or regional 

level depending on size and governmental organization. Decisions on pavement networks 

managed by State agencies can start at a district level but ultimately are made centrally. 

Primary results from network-level pavement/asset management decisions are the pavement 

infrastructure needs and necessary funding to effectively maintain and preserve them. At other 

network-level strata, decisions are made on which segments of the road network are scheduled 

for future construction interventions requiring project-level plans to be prepared. 
4

Because of the economics of pavement condition field data collection, data requirements to 

support network-level business decisions must be more aggregated and contain fewer details as 

one progresses from local- to national-level network strata. 

For pavement assets, decisions at the project level focus on providing the most cost effective, 

feasible, and original design, maintenance, rehabilitation, or reconstruction strategy possible for 

a selected section of pavement within the available funds and other constraints.

(3) The data needs 

at the project level are the greatest since they result in engineering plans and specifications on 

treatment activities to be applied to individual projects.

Alternative contracting mechanisms such as design-build-operate and warranty construction are 

subsets of the more general project-level business decisions and could be classified as a contract 

administration level type of business decision. Because the acceptance conditions must be 

specified in the contract, data support for these project-level contracts have an additional burden 

of withstanding the test of potential contractual challenges. 

ROLE OF RSL IN PAVEMENT BUSINESS DECISIONS

Predicting the remaining life of a pavement is a fundamental aspect of pavement management 

planning. Knowing or estimating the future condition of pavement segments is the rational basis 

of all informed pavement infrastructure planning decisions. A goal of modern pavement 

management systems (PMSs) is to optimize agency resource expenditures while minimizing 

impacts on facility users and the ecosystem. 

Pavement infrastructure budget optimization techniques require predicting the change in 

pavement condition within a defined set of timeframes. Those techniques then predict what 

is needed under the following action scenarios:

• The do nothing alternative, where no physical road construction event is needed 

within the planning horizon since the pavement structure is expected to still be in 

acceptable condition. 

• Applications of routine reactive maintenance to correct spot deficiencies.

• Application of preventive maintenance and preservation activities designed to extend 

pavement service life without significant pavement structural changes prior to the time 

when reactive maintenance treatments are required.

• Application of alternative rehabilitation treatments.

• Pavement reconstruction.

Table 1 presents a summary of the role of RSL models in business decisions at different levels of 

pavement management, contract construction, and pavement operation mechanisms.
5

Table 1. Role of RSL models in levels and types of pavement management 

business decisions.

Decision 

Level Decision Pavement RSL Model Role

Project design

Rehabilitation

treatment selection

Time until pavement structural and functional limit 

thresholds are reached or exceeded; service life prediction of 

candidate treatments 

Overlay thickness 

design

Service life prediction of candidate overlay design 

thicknesses 

Non-overlay 

rehabilitation 

treatments

Time until pavement structural and functional limit 

thresholds are reached or exceeded; service life prediction of 

candidate treatments

Noise mitigation 

treatment selection

Prediction of increase in noise as a function of pavement age, 

surface texture, traffic volumes, and vehicle speed

Friction mitigation 

treatment selection

Prediction of changes in friction as a function of predicted 

change in surface texture characteristics and age 

Pavement 

network 

planning

Needed pavement

maintenance budget 

Time until pavement functional limit threshold are reached 

or exceeded; predicted pavement condition as a function of 

funding allocation

Needed pavement 

rehabilitation budget

Time until pavement structural and functional limit 

thresholds are reached or exceeded; changes in pavement 

condition for non-treated projects and rehabilitated projects

Needed pavement 

reconstruction 

budget

Time until pavement structural and functional limit 

thresholds are reached or exceeded; change in pavement 

condition for non-treated projects and reconstructed projects 

Budget 

optimization—

allocation of 

resources to projects

Change in pavement condition for non-treated projects, 

rehabilitated projects, and reconstructed projects

Contract 

maintenance

management

Contractor

award fees/penalties

Structural and functional service life prediction as a function 

of applied maintenance treatments

Contract 

design/build/

operate

Acceptance by 

public agency at end 

of contract; decisions 

based on contract 

terms

Time until pavement functional need thresholds are reached 

or exceeded; predicted maintenance-free life; time until 

rehabilitation is required; predicted performance of pavement 

until rehabilitation required

Warranty 

construction 

contract 

Acceptance by 

public agency at end 

of contract; decisions 

based on contract 

terms

Time until pavement functional need thresholds are reached 

or exceeded; predicted maintenance-free life; time until 

rehabilitation is required; predicted performance of pavement 

until rehabilitation required
6

As described in table 1, the central role of RSL models in all of these pavement management 

business decisions is predicting the change in pavement condition as a function of time, traffic 

loading, and environment. The basic difference in the RSL models used at the project, network,

and contract administration levels are data requirements related to level of technical detail, 

extent, quality, precision, and accuracy of the model inputs. Project- and contract-level models 

require the greatest amount of input data to satisfy statistically based inferences. Ideally, models 

used at the project level are expected to be calibrated to local conditions and use better and more 

data. As a result, they are perceived to be more accurate than models used at network levels since 

actual engineering decisions are based on their results. Network-level decisions typically result 

in an allocation of resources, which must be subsequently programmed down to the project level. 

Since pavement network condition is only one input in this process, data requirements do not 

currently justify the intensity of project-level data collection. 

When the promise of fully automated pavement data collection technology is finally reached, 

project- and network-level decisionmaking can converge to use a common set of RSL model 

inputs. The basis of the decisions made at the network planning level will then converge with 

information used at the project implementation level. Since simplified models are currently used 

for network-level planning, the assumptions made during the optimization process on the type 

of future treatments to apply to each pavement segment may not match the actual pavement 

treatments designed using detailed project-level data. Until the disparity between assumptions 

used in network-level budget optimization algorithms are matched to the resulting project-level 

treatment decisions, true network budget optimization is not assured. While this vision of 

merging network- and project-level pavement management modeling may be somewhat 

optimistic at this point in time, efforts to reduce network-level data collection costs while 

providing more information are continuing to be pursued. 

ISSUES WITH CURRENT RSL TERMINOLOGY

While the prediction of time until a corrective or preventative construction treatment should be 

applied is an established critical component at all levels of pavement management decisions, 

many issues exist in the current RSL terminology and resulting numerics, which confuse, 

confound, and complicate proper interpretation, interagency data exchange, and use.

One common RSL definition is the time until the next rehabilitation or reconstruction event. 

Rehabilitation and reconstruction are two very different events in terms of pavement condition 

at the time of construction and construction costs. The rule of thumb is that rehabilitation 

treatments should be applied before a pavement has suffered too much structural damage. 

Otherwise, the rehabilitated pavement structure will not last very long. Reconstruction treatments 

are generally warranted after a pavement has reached an advanced degree of deterioration. 

Typically, during the planning process, an agency decides to apply a rehabilitation treatment to 

extend the time until reconstruction is required. Attempting to interpret combined RSL estimates 

from mixed rehabilitation and reconstruction units can cause confusion for decisionmakers. 

Another common RSL definition is the time until a condition index threshold limit is reached. 

This approach shares the same issues as rehabilitation and reconstruction RSL units but also 

introduces other service and safety condition indices, which further complicate the meaning of 

RSL. Setting threshold limits for pavement conditions that are not based on human subjective 
7

ratings, such as cracking, can be complicated to justify. Moreover, interpretation of a single RSL 

number gets even more complicated when it is based on multiple condition states. For example, 

if RSL for roughness is 2 years, RSL for cracking is 5 years, RSL for friction is 7 years, and RSL 

for rutting is 20 years, expressing that the current pavement RSL equals 2 years can lead to 

imperfect construction decisions since the construction treatment selected to correct roughness 

may not necessarily address the more serious cracking issue expected to occur soon after the 

roughness threshold is reached. Since there are many construction treatments that can be used to 

correct excessive pavement roughness that can be classified as pavement preservation, this 

approach adds maintenance-type activities to RSL units. 

Another intriguing aspect of RSL based on threshold limits is negative RSL. When a Pavement 

Condition Index (PCI) limit is reached, from a numerical standpoint, the years it remains in 

service after this time could be considered a negative service life, which is counterintuitive. One 

approach is to set negative values to zero, thus not allowing a negative RSL value to be provided 

by the process. Another approach is to consider negative RSL as overdue needs, in which case,

the number of years overdue can be considered as additional information to decisionmakers if 

they know the basis of the condition in need of attention. 

Another approach to RSL is based on agency management rules on the time between 

applications of corrective pavement construction treatments. For example, a State highway 

agency (SHA) with a relatively small number of interstate highway lane-miles may decide, based 

on past performance, to apply a resurfacing, rehabilitation, or reconstruction treatment every 

8 years to each construction segment unit on the system to keep their highest level functional 

class pavements in the best condition. The RSL becomes the difference in time between the 

construction frequency established and how long it has been since the last treatment was applied.

While this approach simplifies the decisionmaking and project selection process, it does not 

typically result in the most cost effective solution.

One unintentional consequence of using current RSL terminology, which is defined as the time 

to reconstruction or major rehabilitation, is that it tends to promote worst-first approaches to 

correcting pavement deficiencies. By expressing pavement condition in terms of RSL, laymen 

and politicians expect that pavements in the worst condition get treated first. Construction 

treatments on pavements in the worst condition tend to cost the most. Applying a life extending 

corrective rehabilitation treatment before the pavement condition gets too bad tends to cost 

less than reconstruction treatments. Optimum allocation of annual pavement resurfacing, 

rehabilitation, and reconstruction budgets will be a mixture of pavements with differing 

remaining lives and not based solely on a worst-first approach. 

FRAMEWORK PRESENTED IN THIS REPORT

The framework presented in this report is intended to provide a common definition that may be 

referred to by anyone attempting to evaluate the remaining life or service life of a pavement 

structure. Chapters 2 and 3 discuss the updated vocabulary as well as the framework associated 

with RSL development and construction needs assessment. The remaining chapters cover each 

step of the process including construction triggers, threshold limits, performance curves, inputs, 

strategy selection, assessments, and updates. The final chapter summarizes the major findings.

9

CHAPTER 2. REFOCUS OF PAVEMENT REMAINING LIFE VOCABULARY

REFORMULATING RSL TERMINOLOGY

A primary objective of this report is to provide a definition of pavement remaining life that will 

promote consistency in the use of the terminology. Many RSL definitions are currently used in 

the pavement community to describe different events in the construction history of a pavement. 

Construction-related history best describes the use of RSL models in all levels of the pavement 

management decision process because the primary purpose of predicting remaining pavement 

service life (or life within the context of modern pavement management) is to plan for future 

field construction event(s), whether it be the application of maintenance, preservation, 

rehabilitation, reconstruction, or other treatments to correct some attribute of the pavement 

structure. Regardless of the name given to different treatments, these are all construction events 

that cost the highway agency to provide and impact facility users. Ideally, the definition of RSL 

should be independent of business decisions. 

The major source of uncertainty in the current RSL definition is the use of the term “life” to 

represent multiple points in the construction history. In the pavement design context, “life” is 

used to represent the time until the hypothetical pavement structure reaches an unacceptable 

condition since the pavement designer must make assumptions on the pavement properties. 

In the pavement management context, after construction of the pavement structure, the 

as-constructed properties become more important in pavement life expectations than the 

assumed inputs into the original design process. A pavement structure can be thought of as a 

system whose components include subgrade treatments, subsurface drainage features, base 

layers, shoulders, bound structural load bearing layers, and surface layers. As a repairable 

system, the life of the system is not defined by correctable component failures. 

The proposed solution to the problem is to remove the word “life” from the lexicon since it is 

the basis for confusion. Instead of using RSL or structural life, adopting terminology of time 

remaining until a defined construction treatment is required to replace the generic ill-defined 

RSL term. This terminology has the ability to unify the outcome of different approaches to 

determine needs by focusing on when and what treatments are needed and the service 

interruption created. Thus, RSL is replaced by RSI.

Moreover, adopting a definition related to construction treatments creates new terminology for 

treatments related to other factors besides pavement condition. For example, if a construction 

cycle is defined in terms of time until the next construction event requires lane closures, then 

capacity improvements, shoulder widening, utility construction, and realignment construction 

activities can be included in the construction event. In turn, this broadens the application of the 

definition in the future. In some situations, capacity issues can have more of an effect on the 

service provided by a pavement structure than the condition of the pavement surface. This shifts 

the emphasis on the life remaining in a pavement structure to the time remaining until the next 

planned construction lane closure is required or future type of defined construction treatment 

is needed. 
10

FUNDAMENTAL PAVEMENT PLANNING ELEMENTS

The fundamental elements required to unify RSL terminology include the following:

1. Development of a high order controlled vocabulary used to define pavement construction 

events. The objective of this vocabulary is to uniquely define what type of predicted future 

construction event is need.

2. A common basis for when the future construction event is needed. 

3. How future needs are determined to differentiate between different levels of 

business decisions. 

4. The location and extent of the needed treatment.

The logic of this structure is based on separating the definitions of what future construction event 

is needed from how the need is determined. Although project-level prediction methods require 

more intensive engineering data than network-level models, they are used for the same purpose 

in determining similar types of future treatment needs. 

Another need for a fundamental RSL model is to incorporate the service interruption concept. 

Drawing from the concept of a repairable system used outside of the pavement industry, 

incorporating service interruption into the model and quantifying the effect on user experience is 

critical. Thus, some form of user experience (i.e., analogous to functional condition percepts) 

should be a part of a fundamental RSL definition. In addition, there is the need to model 

performance from an engineering standpoint, and this primarily involves integrating a structural 

component into the model. Each of these perspectives must be accounted for as part of 

fundamental pavement planning activities. 

CONSTRUCTION EVENT TERMINOLOGY

The proposed replacement for RSL (i.e., remaining structural life) is RSI (i.e., time until a 

construction treatment is required). This terminology requires three attributes: time when a 

treatment is needed, type of construction treatment, and reason for the construction treatment. 

The time, or the year, when a treatment is needed is specified as this establishes the funding 

needs by budget planning years. This is meant to replace prediction models based on traffic 

applications. Traffic application rates used in the modeling process need to be converted to a 

time basis. Converting traffic application rates to a time basis is a complex process based on 

consideration of the design lane (which receives the most truck loadings), multilane facilities, 

damaged lanes (which are the lanes in worst condition), and other local factors that influence 

pavement damage from vehicle and environmental effects. 
11

Consistent terminology is needed to describe the construction treatments, promote database 

integration, and increase the level of aggregation at local, district, State, and national levels. The 

following examples highlight construction event definitions based on the expanded paradigm of 

common pavement improvements included in many modern PMSs: 

• Crack sealing: Application of sealants in surface cracks.

• Joint sealing: Application of sealants in preformed joints.

• Surface treatment: Application of a layer of material of intended uniform thickness less 

than 0.5 inches (12 mm). 

• Thin overlays: Application of a material layer of intended uniform thickness greater 

than 0.5 inches (12 mm) and less than 2 inches (50 mm) and which does not increase the 

thickness of the bound material layers by more than 25 percent. 

• Thick overlays: Application of a material layer of intended uniform thickness greater 

than 2 inches (50 mm) or increases in thickness of bound pavement layers by more than 

25 percent.

• Concrete pavement restoration: Application of full-/partial-depth joint repairs, slab 

replacement, dowel bar retrofit, or other restoration treatments. 

• Grinding: Removal of portions of the surface layer of a pavement without placement of a 

new material layer.

• Grooving: Cutting of grooves in the surface of a pavement without application of a new 

material layer.

• Milling: Removal of bound portions of a pavement that is associated with placement of a 

new material surface layer.

• Undersealing: Injection of cementitious material underneath bound pavement layers.

• Reconstruction: Removal and replacement of all bound layers of an existing pavement.

• Addition of lanes: Construction of additional lanes to the facility designed to permit 

greater traffic capacity. 

• Addition of tied shoulders: Removal and construction of portland cement concrete

(PCC) shoulders tied to adjacent PCC pavement structures.

• Shoulder widening: Extending the width of the existing shoulder with use of 

similar materials.

Note that the definitions only attempt to describe what type of construction treatment is being 

applied to the pavement. 
12

An indication of the reason(s) why a future construction event is predicted is needed to complete 

the definition since pavement improvements are based on different needs. The following 

examples highlight controlled terminology that can be used to explain the basis of predicted 

time to a threshold event:

• Roughness exceeds y International Roughness Index (IRI). The y value is the generally 

accepted limiting value for pavement roughness in terms of IRI.

• Cracking exceeds the limit requiring major rehabilitation.

• Cracking exceeds the limit requiring reconstruction.

• Rut depth correction requires major rehabilitation.

• Rut depth correction requires reconstruction. 

• Skid resistance reaches the safety limit.

• PCI reaches threshold x. For systems based on a PCI-based index, x represents the 

various thresholds between rehabilitation and reconstruction. 

• Present serviceability rating (PSR) reaches x. For systems still using the PSR/Present 

Serviceability Index (PSI) concept, x is the serviceability value considered by the agency 

appropriate for the route classification. 

Typically, an agency will develop some sort of decision matrix to use as part of its pavement 

management. This matrix will associate types of pavement deficiencies requiring construction 

actions with types of construction best suited to correct them. For example, if the roughness 

exceeds the IRI threshold, then a typical approach would be placing an overlay to correct 

the roughness. 
13

CHAPTER 3. FUTURE CONSTRUCTION NEEDS ANALYSIS FRAMEWORK

INTRODUCTION

Consistent terminology is necessary to reduce potential confusion over the use of the term RSL. 

General guidelines on how to formulate future pavement construction needs analysis are 

provided in this report. The construction needs addressed in this report are associated with 

correcting pavement surface and structural deficiencies, although many of these concepts could 

be adapted for construction related to other needs such as capacity and shoulder improvements. 

The basic process to determine future pavement construction needs is illustrated in figure 1. 

Most pavement construction activity planning is based on an annual fiscal time cycle used by the 

agency. The steps in this figure are cyclical and depend on the time cycle appropriate to the type 

of pavement asset. The process starts with input data, which are fed into the performance models

that predict future changes in the construction trigger models. The outputs from the predictions 

are used to select the most appropriate construction strategy, which is used to develop 

construction plans and specifications. The feedback cycle starts with documenting the actual 

condition observed over time as well as the actual construction activities performed. Monitoring 

measurements are performed to provide updated inputs for the next planning cycle. 

Figure 1. Flowchart. Future pavement construction needs process.

While the basic framework illustrated in figure 1 starts with inputs, the input requirements 

are based on the model used for construction triggers. The framework discussion starts 

with construction triggers, which are the basis for setting threshold limits and establishing 

performance models for factors used to select corrective construction strategies (see chapter 4 

for additional information). 
14

The following topics must be addressed to develop, implement, maintain, and update a 

construction needs analysis methodology:

• Construction triggers.

• Threshold limits.

• Performance curves.

• Collection of inputs.

• Strategy selection.

• Assessment and update. 

The methods should be tailored to individual agency requirements related to the budgeting 

process, types of pavements in use, common types of pavement deficiencies requiring correction, 

construction contract instruments, and other considerations. 

CONSTRUCTION TRIGGERS

A critical step in the construction needs analysis process is to determine the most appropriate 

pavement factors that should be used to indicate the application of pavement construction 

treatments. The selected set of construction triggers constitutes the basis for all of the other 

activities used in the construction needs analysis. 

Construction triggers may be distress related (i.e., used to identify specific target levels of 

cracking, roughness, rutting, or friction) or related to capacity of the roadway (i.e., adding lanes 

to manage increased traffic levels).

THRESHOLD LIMITS

The next step in the process is to set threshold limits on the pavement factors selected as 

construction triggers. Crossing a threshold limit can indicate the need for a construction 

treatment. Pavement condition assessment is complicated with the use of hierarchical 

threshold limits of construction needs for the same condition factor. 

PERFORMANCE CURVES

Because the goal of this activity is to plan for future construction events, some form of 

performance curve is needed to forecast the changes in each construction trigger since the last 

condition measurement. Performance curves on the rate of change in pavement condition can 

never be static due to changes in traffic loadings, climate effects, construction techniques and 

materials, and technology advancement. Keeping pavement performance curves updated with 

current technology is an apparent intractable problem since long-term observations are generally 

required to understand how new technology performs. 
15

COLLECTION OF INPUTS

Ideally, pavement management agencies should only collect data needed to support their 

decisions. Inputs needed to support future pavement construction needs include the condition of 

construction triggers, explanatory variables used in performance curves, basis of threshold limits, 

and construction costs. 

STRATEGY SELECTION

The strategy selection process is based on future construction needs when the expected condition 

state of a construction trigger exceeds a threshold limit. Secondary processes include estimation 

of construction costs and pavement performance expectation after application of each alternative 

construction treatment so that cost-benefit-based optimization calculations can be performed. 

ASSESSMENT AND UPDATE

All management processes require a formal system that documents current procedures, provides 

an independent assessment of adequacy and compliance with established procedures, and 

includes an improvement update process to keep them relevant and technologically current. The 

formal quality management standards developed by national and international agencies can be 

used as the basis to develop agency-specific protocols. 

17

CHAPTER 4. CONSTRUCTION TRIGGERS

INTRODUCTION

Construction triggers are measureable aspects of a pavement’s condition that can be used 

to indicate the need for corrective treatment. Selecting construction triggers is the basis for 

developing field data collection programs to measure the condition state of pavement segments.

Some considerations that should be taken when selecting construction triggers include

the following:

• Historical practice: This is the starting point for most agencies since time-history 

pavement condition data are needed to develop and modify performance curves, which 

are discussed later in this report.

• Related agency practice: Piggybacking on practices of related pavement management 

agencies is a strategy that can be used to potentially reduce field measurement, 

engineering, development, and software costs. Related pavement agencies are those that 

are located in the general geographical area, use similar materials, and have commonality 

in construction practices/pavement contractors. For example, there may be areas of 

overlap between city, regional, and State transportation authority’s managing pavements 

with common attributes that form a basis for information and technology interchange. 

• Extent of the pavement network being managed: The greater the number of lane-miles 

being managed, the greater the need for automation to reduce condition measurement 

costs. Manual pavement condition measurements performed by crews typically cost 

more, are slower, and introduce greater safety considerations than automated methods. 

• Data collection budget: The majority of construction triggers are based on some type 

of field pavement condition measurement. The type, extent, and frequency of condition 

measurements are based on the data collection budget and incremental cost-benefit of the 

data collected relative to the construction decision process. 

• Required measurement accuracy, precision, and detail: The required accuracy, 

precision, and detail of pavement condition measurements are related to the use of the 

information. Project-level measurements, which are used to develop construction plans 

and specifications or are used for contractual acceptance purposes, require the best 

available standard of accuracy, precision, and information detail content. Network-level 

measurements, which are used to provide a coarse filter of the condition of all pavements 

included in the system, generally require less detailed information content and lower 

levels of precision. 

• Functional class of pavements in the network: Functional classification of pavements 

is primarily based on the route locations, role, volumes, and governmental classification 

in the pavement transportation system. Location is classified as urban or rural. Role is 

classified as local, collector, arterial, or interstate. Traffic volume is used to differentiate 
18

between minor, major, and principal routes. Generally, as the level of functional 

class progresses from rural local collector roads to urban principal arterial interstates, 

pavement structures also change in thickness, strength, materials, and base needed to 

support the increased loads. 

• Pavement types or pavement families: A pavement family is a group of pavement 

structures constructed with similar structural materials, construction methods, pavement 

components, and experience loading conditions and are expected to have a common set 

of distress mechanisms. 

• Common distress manifestations: Patterns found in the common types of distresses 

experienced in pavements in the managed network can be used to refine the set of 

construction triggers requiring field data collection. This type of refinement can be based 

on an analysis of what project-level data are most critical to the selection of corrective 

construction treatments. A successful strategy is to reduce network-level pavement 

condition measurements to those that most influence corrective construction within the 

managed network. 

• FHWA Highway Performance Monitoring System (HPMS) reporting requirements:

SHAs required to submit pavement condition data on HPMS sample sections in their 

jurisdiction to FHWA can leverage the Federal research and development resources being 

allocated to update and modernize the national system of pavement infrastructure needs 

analysis. SHAs can use the findings from these FHWA research and development efforts 

to improve their pavement construction needs planning processes. 

LEVEL OF SERVICE

Level of service construction triggers are primarily based on human factor ratings of the 

pavement serviceability and pavement roughness measurements. 

The use of pavement roughness as a primary indicator of level of service is based on the 

American Association of State Highway Officials (AASHO) Road Test conducted in the 

1950s.(4) Participants provided a PSR on a set of test sections whose attributes were also 

measured using the best available objective technology at the time. A PSI predictive equation 

was developed to estimate PSR from the objective ratings. The pavement roughness component

accounted for about 85 percent of the variation in the PSR ratings, which verified the expectation 

that pavement roughness was the primary determinant of pavement level of service from a user 

standpoint and provided a basis to scale the pavement roughness measurement numerics. 

Although the AASHO Road Test PSI equation also included statistically significant terms for 

cracking and patching, the pavement industry has evolved to where pavement roughness, 

expressed in terms of the IRI, is the most common measure of pavement level of service.

PAVEMENT SURFACE DISTRESS

The type, extent, and severity of pavement surface distresses are the basic elements used to 

describe pavement distresses. The fundamental classifications of pavement distress are related 

to fracture, deformation, and disintegration of the pavement material. 
19

While national standards exist for more than 15 possible types of pavement distress attributes for 

each type of pavement, they are not all typically required for pavement construction decisions. 

Reducing the number of distresses to a small number of core distresses can reduce field data 

collection costs. Methods that can be used to create construction triggers based on pavement 

distress measurements include the following:

• Survey the predominant types of distresses common to an area or region that deteriorate 

the quickest and require construction intervention to correct. Typically, pavements that 

are constructed in a jurisdiction using similar materials and that are subjected to the same 

environment have similar patterns of distress.

• Use the predicted distress types from the pavement design method. 

• Create or use an existing numerical index based on assigned deducted values for the type, 

extent, and severity of a selected range of distresses.

• Develop a correlation between distresses and level of service indicators.

• Associate distress types with corrective treatments. For example, potholes can 

be corrected with patches, while severe fatigue cracking usually requires 

pavement reconstruction.

STRUCTURAL CONSIDERATIONS

Pavement structural considerations are based on certain types of distress and non-destructive 

pavement deflection testing. 

The primary distinction of distresses most often associated with structural pavement damage 

is those whose mechanism of formation is due to the application of wheel loads. Common 

distresses associated with pavement structural integrity include cracking in the wheel path 

(fatigue cracking), corner cracks on jointed PCC pavements, faulting on jointed PCC pavements, 

punch outs on continuously reinforced concrete pavement, and rutting associated with subgrade 

and base instability. A secondary consideration is distress types that contribute to the 

acceleration of structural damage. 

While surface cracking can be an indicator of pavement structural damage, some pavement 

preservation treatments hide surface cracks. Deflection measurements can be used as a 

diagnostic tool to look below the pavement surface to determine subsurface damage. 

Pavement deflection measurements can be interpreted to characterize a variety of pavement 

structural conditions. The basic concept behind deflection measurements is the measurement of 

the deflection of the pavement surface under the application of a known load. Information can be 

obtained from devices that measure the deflection basin at various distances from the load center 

and from devices that apply multiple load levels at the same measurement point. A comparison 

of the changes in deflection response and associated computed parameters along a pavement can 

reveal the extent of damage not yet visible. 
20

Pavement structural information from deflection measurements includes the following:

• Computation of the modulus (stiffness) of the pavement foundation layer (subgrade) from 

a forward calculation. 

• Indicators of the relative stiffness of the surface and base layers using deflection 

basin indices.

• Load transfer across joints and transverse cracks in PCC pavements.

• Stiffness characterization of distinct pavement layers from a backcalculation, which 

requires layer thickness information. These properties can be used as inputs to a variety 

of pavement performance prediction models.

• Computation of the effective structural capacity based on the 1993 American Association 

of State Highway and Transportation Officials (AASHTO) Guide for Design of Pavement 

Structures methodology.(5) Structural capacity is defined as the structural number (SN) 

for flexible pavements and thickness of the PCC layer for rigid pavements.

SAFETY

Safety aspects of a pavement condition are primarily related to friction and hydroplane potential.

Pavement friction characteristics are most often characterized using a skid number parameter. 

These parameters are based on field measurements using locked wheel, limited slip, or yaw 

mode friction testers. Hydroplane potential is related to the ability of pavement ruts to hold 

water. The nominal depth of water retained in the ruts can be associated with hydroplane 

potential based on the speed limit. 

Another safety concern is excessive pavement roughness relative to the speed limit. It is 

possible for localized bumps, dips, faults, and holes in the pavements to negatively influence 

vehicle control. 

AGENCY TIME-BASED RULES

One of the simplest construction needs triggers is the time since the last construction treatment. 

Some agencies have implemented rules on the maximum amount of time between construction 

events. Time-based rules are intended to reduce field measurement costs and provide a proactive 

pavement management approach to keep pavements in good condition. 

TRAFFIC CAPACITY

In changing the definition of RSL to one that is related to assessing future construction needs, it 

is important to identify when roadways need to be expanded by adding new lanes due to traffic 

growth. These types of rules are generally outside the realm of pavement management, but they 

should be considered when developing an estimate of future budget needs.
21

CHAPTER 5. THRESHOLD LIMITS

INTRODUCTION

Threshold limits are used to indicate when a construction trigger reaches a condition when a 

corrective or preventative construction treatment is needed. This chapter presents various 

methods and procedures that can be used to establish threshold limits. 

In this discussion, maintenance and preservation maintenance treatments do not include routine 

and catastrophic maintenance. The activities included in this category are planned projects that

include pavements longer than 4 mi (6.4 km) that are designed to extend the time until 

rehabilitation and reconstruction treatments are required. Since the distinction between 

maintenance, preservation maintenance, and rehabilitation treatments is often dependent on 

funding sources, the definitions used by the funding sources for allowable treatments should be 

incorporated into the development of threshold limits. 

Rehabilitation treatments include extensive restoration treatments and structural overlays. The 

general definition of structural overlays is the addition of a new material layer whose thickness 

is greater than 15–25 percent of the existing bound pavement layers. 

A common definition of pavement reconstruction is the removal and replacement of all existing 

bound pavement layers. This definition includes situations where removal and replacement of the 

unbound base and subbase layers are also required in addition to the bound surface layers. 

SUBJECTIVE THRESHOLD LIMITS

Subjective threshold limits are based on ratings determined by panels of judges that include

laymen facility users, pavement experts, or a combination. Generally, a formal rating scale is 

created and used by the judges, and statistical methods are then used to interpret the ratings and 

establish limits. 

Subjective ratings can be used to define an absolute acceptable limit or degrees of acceptability 

for a measured condition attribute. The following acceptance scales can be used to capture 

subjective panel ratings:

• A binary or two-level acceptable/not acceptable or pass/fail rating scale: While 

this type of forced choice acceptance scale purposefully limits the range of response 

to identify the acceptance threshold, it also limits analysis of the results. 

• A five-level Likert scale: This type of scale provides a measure of the range in 

acceptance criteria to be considered. An example rating scale for the condition of a 

pavement test section is as follows:

o 5 = Totally acceptable.

o 4 = Slightly acceptable.
22

o 3 = Neutral or not sure.

o 2 = Unacceptable.

o 1 = Totally unacceptable.

A key feature of this type of rating scale is that it allows greater options in the analysis 

of the ratings. The neutral center point value is a measure of the gray area between 

acceptance and non-acceptance. The ratings can be converted to a three-level scale 

by combing the two acceptable ratings and two unacceptable ratings. 

• A four-level Likert scale: The purpose of the four-level scale is to remove the neutral 

middle rating to force raters to provide either an acceptable or unacceptable rating. In 

some cases, trying to interpret the meaning of neutral ratings can be confounded with 

factors not related to the pavement condition of interest. An example of a four-level scale 

is as follows:

o 4 = Totally acceptable.

o 3 = Slightly acceptable.

o 2 = Unacceptable.

o 1 = Totally unacceptable.

• The degree of acceptability can also be based on a Likert scale by altering definitions of 

the ratings as follows:

o 5 = Very poor.

o 4 = Poor.

o 3 = Fair.

o 2 = Good.

o 1 = Excellent. 

In this example, to improve the repeatability of the results, it is useful to provide the raters with a 

more refined definition of each category that is related to the attribute of interest. The cardinal 

ordering of the scale can also have an impact on the scoring. In this example, the largest value is 

assigned as the worst condition state, although most grading scales use the largest value to 

represent the best condition state. 

Subjective ratings can be influenced by a wide range of factors, some of which may not be 

desired or can overly influence the ratings. For example, road roughness is commonly expressed 

using the IRI parameter computed from measured longitudinal road profiles. A method to 

determine levels of acceptability to riders is to have a panel rate the acceptability of a set of test 
23

sections with a range of roughness by driving over them. In this case, the following factors can 

influence the ratings provided by the panel:

• Suspension characteristics of the vehicle used by the rating panel: People riding in 

vehicles with soft suspensions will feel less vibration than those riding in stiff 

suspension vehicles.

• Vehicle speed: The suspension response of a vehicle is greatly influenced by vehicle 

speed. Since IRI is standardized to a constant speed, test sections with the same IRI can 

have different degrees of acceptance depending on how fast the vehicle is travelling. 

• Distress condition or appearance of the test section: While the purpose of the rating is 

to judge the level of pavement roughness, it is known (from the AASHO Road Test and 

other studies) that raters can be influenced by other factors such as the amount of 

cracking and visual appearance of the test section surface.(4,6)

• Climate conditions during rating sessions: High winds can cause movements in the

measurement vehicle that are not due to the roughness of the pavement. 

• Quietness of the test vehicle: The noises heard while driving can be more related to the 

texture characteristics of the pavement surface than the pavement roughness.

• Type of pavement: The roughness producing characteristics of a jointed PCC pavement 

are different than those of an asphalt concrete (AC) pavement. 

• Alignment of the pavement test sections: Horizontal curves, cross slope changes, and 

vertical grades can influence the response of the rating vehicle. 

• Driver of the rating vehicle: The method the driver uses to maintain speed can influence 

the response of the rating vehicle. 

A field experiment should be developed to attempt to control or measure the statistical 

significance of cofactors that can influence subjective ratings. 

ENGINEERING CONSIDERATIONS

Engineering considerations used to establish threshold limits are based on pavement performance 

mechanistic concepts or pavement-vehicle interaction factors. Examples of engineering 

considerations include the following:

• It can be postulated that when cracks extend completely through the bound layers of a 

pavement, they allow entry of surface water into the unbound layers. Accumulation of 

water in the unbound layers can advance the rate of pavement deterioration and cause 

damage that is expensive to repair. Applying a corrective treatment before the cracks 

reach this state can help preserve the integrity of the foundation support and extend the 

time until rehabilitation or reconstruction is needed. 
24

• Research has shown that hydroplaning potential of a vehicle is related to the depth of 

water and vehicle speed. It is possible to establish a threshold water depth for the design 

vehicle speed of a route. Assessing the potential water holding capacity of a rut requires 

consideration of more than rut depth. Factors such as shape of the rut, rut location, and 

pavement cross slope can be determined from the modern generation of high-speed 

road condition survey devices that measure the pavement’s transverse profile. 

Computer programs can determine the actual water depth potential from these types

of field measurements. 

• The perpetual pavement concept developed for AC pavements is based on the premise 

that an adequately thick asphalt pavement placed on a stable foundation will resist 

distresses that form at the bottom of a pavement structure and are costly to correct. By 

limiting distress formation to the pavement surface layer, the pavement life can be 

extended in perpetuity by rejuvenating the pavement surface materials in a remove and 

replace type of maintenance operation. The prime consideration in this type of pavement 

system is to plan future construction events to limit the depth of the top-down cracks. 

However, measuring the depth of top-down cracks from project-level field measurements 

is a challenge.

• As pavement roughness increases, the level of dynamic load impacts on the pavement 

structure from heavy trucks also increases. Since mechanistic principles of pavement 

performance are primarily based on the magnitude and frequency of applied truck loads, 

in theory a limit on pavement roughness should exist where the magnitude of the applied 

dynamic loads accelerate the structural damage to the pavement. The algorithm created to 

assess the adequacy of pavement smoothness for location of weigh-in-motion scales is 

based on computation of the increase in the level of dynamic truck wheel loads from the 

same field data used to compute IRI.

• Pavement structural capacity diminishes to the point where stress and strain levels

of applied traffic loads accelerate pavement damage. The results of non-destructive 

pavement deflection testing can be used to assess changes in pavement structural 

properties related to resulting increases in stress/strain responses in critical pavement 

structural layers. 

EMPIRICAL METHODS

Empirical methods are based on observations of events. The historical context of empirical 

methods is based on the scientific process where findings are based on experimental 

observations. Within the current pavement engineering nomenclature, the empirical part of the 

Mechanistic-Empirical Pavement Design Guide (MEPDG) was calibrating the mechanistic 

prediction models to field performance observations.(7) Thus, establishing threshold limits using 

an empirical approach is based on observations of past events, which may or may not be based 

on mechanistic engineering principles. 

A critical aspect of empirical models is that they are most applicable to the inference space of the 

observations from which they were developed. Technology advancements or other changes that 
25

are outside of the inference space of the original observations can limit the applicability of 

existing empirical models to future events. 

Examples of empirical approaches used to set threshold limits include the following:

• Analysis of friction data and associated accidents rates or field experiment to set a safe 

level of friction.

• Statistical analysis of the pavement condition when construction treatments have 

been applied.

The advantage of this type of analysis is that it does not require a thorough understanding of the 

mechanism being modeled. For example, one can postulate that the accident rate on a section 

of roadway is related to the level of friction offered by the pavement surface. One may also 

recognize that the accident rate on this same section of roadway is related to the speed of the 

vehicles traveling on that roadway. With an empirical approach, it is not necessary to fully 

understand all of the mechanisms associated with the accident rate, which may include 

psychological factors associated with the drivers. Rather, the correlation of the friction with 

the accident rate can assist in identifying an unacceptable level of friction on the roadway.

ECONOMIC ANALYSIS

Construction limit thresholds can be developed from an economic analysis of construction timeseries costs over a long-term period. This analysis depends on knowing or estimating how long 

alternative construction treatments will last based on the predicted condition of the pavement 

at the time of the treatment and the cost of the construction treatment. By running Monte Carlo 

simulations with alternative construction treatments performed at different times, a minimum 

worth cost can be found as a function of pavement condition. The length of the analysis time 

period has to be long enough so that the pavement deterioration condition factors eventually 

force a needed construction treatment. 

Figure 2 illustrates the expected outcome of an economic analysis on the most cost effective 

repair strategies as a function of pavement condition for an individual pavement. Plateaus in 

repair costs will exist since the same construction treatment will correct a range of pavement 

distresses and severities. A common construction treatment can be specified for a length of 

roadway based on the worst condition in that segment. The upper plateau represents the cost 

to reconstruct the pavement after it has reached a condition state where maintenance and 

rehabilitation (M&R) treatments do not last long enough to be cost effective. Likewise, the 

rehabilitation plateau represents a common set of treatments that do not require complete 

removal and replacement of all bound pavement layers. The significant concept illustrated in this 

figure is that breakpoints should exist in the pavement condition-repair cost relationship that is 

expected to be unique between different pavements types, load magnitudes, environmental 

conditions, agency repair policies, and construction costs. 
26

Figure 2. Graph. Conceptual relationship between agency repair costs as a function of 

pavement condition.

Critical factors in pavement construction time-series economic analysis include the deterioration 

rate of the pavement, what type of repair treatments are considered, the effect that pavement 

condition has on the resulting performance for each repair treatment, and costs included in the 

analysis. To avoid manipulation of the results from pavement life-cycle cost analysis (LCCA) 

due to the assumptions of the person performing the analysis, SHAs need to create a set of rules 

to be used in this type of analysis. To the extent possible, the rules should be based on

observations from pavements under agency jurisdiction. However, repeated findings from 

research studies on this topic indicate that not enough field data exists to create these models 

with certainty. As a result, agencies may need to use engineering judgment based on available 

data to create the rules. Establishment of a preliminary set of rules provides a basis to evaluate 

and update the rule set based on experience. Part of the rules should be standard estimates of 

error and error distributions forms for use in stochastic/risk-based analysis. 

COMBINATION OF APPROACHES

The development of successful threshold limits may best be accomplished through a combination 

of the approaches previously described. Pavements are complex structures designed to serve a 

vast array of vehicles operated by users with differing requirements. Development of threshold 

limits on various measures of pavement condition to indicate the need for a corrective 

construction treatment must also take into consideration management agency policies, agency 

managed infrastructure asset-to-budget ratio, multiple funding sources, and user expectations 

relative to the role of the route in the local transportation system. 

The recommended approach to setting threshold limits on need for corrective construction 

intervention is by use of a combined engineering economic approach. The objective should be 

to determine condition states where maintenance or preservation treatments, rehabilitation 

treatments, and reconstructive treatments are most cost effective. 
27

Considerations to determine appropriate pavement state thresholds for application of 

maintenance or preservation treatments include the following:

• Conventional practice is that preservation or preventive maintenance treatments should 

be applied while a pavement is still in relatively good condition. In theory, pavements 

still in excellent condition with no defects should not require treatments. The issue 

becomes determining when treatments are best applied. When possible, the upper limit 

should be based on specific pavement defects which can be corrected to a good-as-new 

condition without the need for structural restoration treatments. If aggregated distressbased indices are being used, then use of expert subjective opinions can be used to 

determine a nominal threshold value when preservation maintenance activities can 

be used. 

• One strategy for setting threshold limits for pavement condition to determine when 

preservation treatments are applicable is by considering when the extent and severity 

of distress types have reached levels requiring corrective treatments for structural 

improvements and preservations treatments are no longer adequate. There is a point when 

the extent of the required treatment represents a structural improvement or crosses the 

capital improvement cost threshold. Some examples include the following:

o The number of locations per mile requiring full-depth repair exceeds 10–30 percent

of the surface area depending on the pavement and distress type.

o Extent of faulting of jointed PCC pavement requires the combination of grinding, 

ultra-thin overlays, and dowel bar retro fit to correct. 

o The severity and extent of alligator or fatigue-related cracking in the wheel paths 

requires full-depth patches on more than 10 percent of the section length. 

o When structural and restoration treatments cost less than alternative spot repairs. 

o When pavement roughness reaches a level that causes a significant increase in vehicle 

operating costs. 

One rule of thumb is that an overlay thicker than 2 inches (50 mm) is considered a structural 

improvement regardless of the depth of material milled from the pavement surface. Another 

common threshold rule used is that when the treatment cost rises to a certain level set by the 

agency, it is considered a capital improvement project and requires formal engineering plans 

and specifications. Like definitions of preservation maintenance, the definition of pavement 

rehabilitation is also dependent on available funding agency sources. The upper pavement 

condition threshold limit for rehabilitation is based on when preservation maintenance is no 

longer effective. Lower limit rehabilitation pavement condition thresholds are based on when 

reconstruction becomes the most cost effective treatment.

There are also situations when structural defects may require reconstruction to correct even 

though all other functional aspects of the pavement are acceptable.

29

CHAPTER 6. PERFORMANCE CURVES

INTRODUCTION

Performance curves are used as a means to predict the time when a pavement’s condition will 

reach a construction trigger threshold. While the results of current pavement condition states can 

be used to respond to conditions, long-term planning of future needs and optimization requires 

the prediction of future pavement changes.

A key consideration in the development of performance curves is grouping pavement types 

into categories of pavement families. A pavement family is a group of pavement structures 

constructed with similar structural materials, construction methods, pavement components, and 

experience loading conditions and are expected to have a common set of distress mechanisms. 

The number of pavement families that should be used depends on the diversity in types of 

pavement structures with an agency’s jurisdiction and the amount of time history data available 

for each defined family of pavements.

The best practice is to base performance curves on the analysis of pavement performance history 

observations. This requires the availability of uniform long-term time-series data on pavement 

condition that are linked to measured pavement features that permits application of mechanisticbased performance models. When empirical data are not available to formulate proper statistical 

models of future performance, expected performance curves based on the best available 

information can be used as a surrogate starting point to judge the relative performance of 

pavements. For example, the knowledge of engineers with long-term experience in a region can 

be used as a surrogate starting point. While the use of expert subject opinion can be used as a 

starting point for creating performance curves, the curves should be updated over time with field 

measurements to improve their accuracy and applicability.

MODELS BASED ON DESIGN EQUATIONS

Performance curves used for pavement design can be different than those used for pavement 

management. The performance curves used for pavement management can be based on empirical 

survivor statistics, which lack explanatory terms for differences in factors considered during 

pavement design. Pavement design performance curves require models that provide a measure of 

predicted future performance as a function of controllable design factors based on uncontrollable 

design requirements. Controllable design factors include man-made aspects of pavement 

structures, while uncontrollable design requirements include changes in climate, future traffic 

loads, and earthquake events. While pavement performance curves used for pavement design can 

be used for pavement management, pavement performance curves developed from pavement 

management data may not be appropriate for pavement design use since they may not be 

sensitive to controlled pavement design factors. A simple example is that a statistical analysis of 

time until pavement reconstruction is not sensitive to pavement thickness because all of the 

pavements included in the analysis had approximately the same thickness. 

Models based on the 1972 AAHSTO Interim Guide for Design of Pavement Structures equations 

and subsequent updates have been used by some agencies.(8) Pavement condition is expressed in 

terms of PSR or PSI, and pavement structure capacity as SN for flexible pavements and slab 
30

thickness for PCC pavements. Using this system, resurfacing or reconstruction is indicated 

by the level of PSR. When the predicted pavement PSR in an analysis cycle drops below a 

minimum tolerable condition based on highway functional classification, then resurfacing was 

indicated. Reconstruction was triggered if the PSR dropped below the lower reconstruction 

threshold if the section was not previously selected for resurfacing. 

While the legacy AASHTO pavement design equations served the industry well by providing a 

basis for the design of the interstate system and other pavements in the United States, their use in 

pavement management has some limitations. Because PSR/PSI is used as a measure of pavement 

condition, it is not a good measure of what future construction treatments are required since it is 

most sensitive to pavement roughness. Updates to the 1972 AAHSTO Interim Guide for Design 

of Pavement Structures equations were made over the years by adding more terms to allow 

greater calibration to local conditions.(8) These updates did not change the fundamental nature of 

using PSR/PSI as the primary measure of pavement condition.

FHWA has developed a set of performance curves based on the MEPDG for use in the Highway 

Economic Requirements System (HERS), National Pavement Cost Model (NAPCOM), and 

pavement health track (PHT) analysis tools.(7) Simplified models based on the use of the default 

level 3 MEPDG inputs along with the HPMS data are used to predict changes in the multiple 

pavement condition measures adjusted for current and past observed levels. The following 

distress prediction models are included in the tool:

• New hot mix asphalt (HMA) and HMA overlay of existing HMA.

o Alligator cracking.

o Rutting.

o Transverse cracking.

o Smoothness—IRI.

• New jointed plain concrete pavement (JPCP).

o Transverse cracking.

o Transverse joint faulting.

o Smoothness—IRI.

• Composite—HMA overlay of existing PCC.

o Reflection cracking.

o Smoothness—IRI.
31

The use of specific types of pavement distresses as construction triggers at the network level 

allows for greater flexibility in assigning and determining the cost of future construction needs. 

This permits the application of automated rational decision tree logic to the selection of 

appropriate treatments based on multiple aspects of pavement condition. 

The use of complex pavement performance prediction models, such as those used in 

the adaptation of the complex MEPDG pavement design models, is an example of how 

detailed research findings can be adapted to the management of pavement assets. The key to 

implementing these complex models, which are based on hourly changes in climate, traffic, and 

material properties, is to simplify them to the level commensurate with the intensity of data 

collection and sensitivity of business decisions.

EMPIRICAL MODELS

Empirical models are based on observations of events. All pavement mechanistic models based 

on the theoretical response of pavement structures to wheel loads and environmental factors 

need to be adjusted to fit field observations. The numbers of factors that have the potential to 

influence the performance of a pavement are greater than can be explained by available 

mechanistic models. While advancements are being made in pavement measurement technology, 

there will always be the need in pavement engineering practice to calibrate theoretical 

considerations to field observations. 

The following list summarizes empirical approaches used to model pavement performance:

• Survivor curves: The various approaches used to develop survivor curves based on life 

table, Kaplan-Meier, or failure time theory approaches (see appendix B of this report) are 

based on the prevailing definition of pavement death as defined by each agency. These 

legacy survivor curves have been replaced with more modern engineering change of state 

models, which can be used to estimate future construction needs not properly reflected by 

the variability in past definitions of pavement death. 

• Failure Cox Proportional Hazard (PH) model: This statistical model form is 

recommended as the basis for future developments in empirical performance curves for 

pavement management applications (see appendix B). The statistical approach appears 

suited to pavement performance management data. The Ohio Department of 

Transportation (ODOT) has used this approach to create a prediction model based on 

their pavement condition rating scale. 

• Numerical regression models: Numerical regression models are used to find the best 

numerical functional form that describes the variation in the relationship between 

variables being compared. Regression models only describe the numerical relationship 

in the available observation data and are most applicable to that inference space. 

Extrapolation beyond the inference space should include an estimate of the magnitude 

of variability in the prediction. 
32

• Bayesian statistical updates: Updating prior models with new data can be based on

Bayesian statistical concepts. A key component in Bayesian statistics is the weight given 

between prior and posterior observations.

• Neural network models: Neural networks are models inspired by the way biological 

nervous systems, such as the brain, process information that have the capability to fit the 

variations in observed data that is not possible by using traditional numerical regression 

techniques. Research to extract information from neural analysis similar to that inferred 

by numerical regression-based model forms is being pursued. 

Some measure of variability distribution for the function and its relevant factors should be 

contained in all predictions of future pavement condition states. These measures can be used to 

accumulate future risk probabilities in the prediction based on the variability in the input 

parameters and prediction models. The FHWA LCC models are an excellent example of how 

variability can be propagated into the final decision tool. 

AGENCY TIME-BASED RULES

The simplest performance curve is a time-based rule for future construction treatment. This type 

of rule does not require investment in field measurement devices and custom computer programs 

to predict future events. However, this approach does not provide a basis for optimization of 

utilization of constrained agency resources.

An example of a time-based rule is that an agency decides that some type of corrective 

construction treatment will be applied on its rural interstates every 8 years. The network is 

segmented into contiguous projects, and a construction rotation sequence is established. An 

appropriate correction strategy is selected and applied during the target construction year. Thus, 

the performance curve for the time until the next construction event is the time remaining until 

the next target construction year. 
33

CHAPTER 7. COLLECTION OF INPUTS

INTRODUCTION

Collection of data on the condition state of pavements under an agency’s jurisdiction should be 

based on the same construction triggers that form the basis for local decisions on corrective 

construction needs. 

Field data collection on present pavement conditions should properly be used to determine the 

impact of the data element on future construction requirements based on the current agencies 

construction triggers. This effort is complicated with the need to adapt past and new pavement 

condition measurement practices. 

The current challenge to SHAs is integrating, adapting, and adopting advancements in 

measurement of the physical features of pavement assets to legacy management systems. The 

development of datasets to establish performance curves of the long-term performance of a 

pavement requires a uniform set of data based on uniform measurements. Since common past 

pavement design practice was based on a 20-year life span, developing datasets with consistent 

data over this type of timeframe is difficult at best. This challenge will continue since emerging 

practice is to design pavements with even longer life spans (i.e., 50 years). 

PAVEMENT ROUGHNESS

The use of high-speed longitudinal pavement profile equipment has become an accepted industry 

standard used to measure pavement roughness. The technique uses an inertial profiler that

measures the change in longitudinal profile in the wheel paths at or near the speed limit. 

Roughness indices are computed from this profile and summarized at user-defined intervals. 

IRI is one of the most commonly computed pavement roughness measures. Other pavement 

condition feature measures and indices can be computed from this type of profile data such as the 

roughness index, half-car roughness index, ride quality index, ride number, profilograph index, 

rolling straight edge simulation, “bump/dip” detection, fault heights on jointed PCC pavements, 

slab curvature on jointed PCC pavements, heavy truck dynamic loading index, etc. 

If pavement roughness measurements are performed using inertial longitudinal profiler devices, 

both network- and project-level data requirements can be satisfied using this common set of data. 

PAVEMENT DISTRESS

Pavement distress ratings can be performed by human raters driving the pavement network using 

a manual process, a semi-automated process where field collected images are interpreted by 

human raters in an office, or fully automated systems. Manual pavement distress measurements 

that require raters to drive each route typically cost the most, put raters at risk (raters must drive 

and get down on the road to note any necessary information), and generally have the greatest 

variability in the ratings. In the semi-automated process, pavement distress ratings are performed 

using field video images, which reduces the risk to the raters and provides a historical archive 

of images for use in project-level investigations. Fully automated pavement distress ratings 

systems uses computer algorithms to interpret distresses obtained from field images and/or 
34

three-dimensional measurements. It offers the potential to reduce the cost of collecting data by 

eliminating the need for human interpretation. The newer three-dimensional imaging technology, 

which assigns a depth to image pixels, provides a more robust dataset that can be used for 

pavement cracking, rutting, and possibly pavement roughness measurements.

PAVEMENT STRUCTURAL RESPONSE

Deflection measurements are used to measure the response of a pavement structure to a known 

applied load. Interpretation of the resulting deflection data ranges from the identification of 

weak spots to advanced non-linear characterization of the engineering properties of pavement 

material layers. 

Pavement deflection measurement techniques vary by the types of applied load, load magnitude, 

deflection measurement characteristics, and traffic control requirements. The types of applied 

loads range between nearly static creep, sinusoidal dynamic loads, impact loads, and wheel 

loads. Many modern deflection measurement devices allow the application of different applied 

load magnitudes that approach the legal limit of truck axle loads. Devices that measure the 

resulting deflection basin as a function of distance from the applied load allow the application of 

engineering algorithms to characterize the material properties of pavement layers. Pavement 

deflection measurement devices currently used in practice must stop at each measurement 

location, which requires traffic control. 

FHWA has supported the development of a rolling wheel deflection measurement device that 

does not require traffic control. While this device can measure the maximum deflection response 

under the moving wheel load, it is not yet capable of measuring the shape of the deflection basin 

around the moving wheel load. While some maximum deflection pavement performance models 

exist based on the old Benkelman beam measurement technology, the shape of the deflection 

basin is currently required for pavement deflection analysis tools. 

To ensure accuracy of data interpretation using pavement deflection response devices, it is 

important to know the pavement thickness and types of near-surface material layers. Manual 

measurement techniques of pavement thickness are being replaced by automated ground 

penetrating radar technology. Network-level pavement deflection measurements and estimation 

of pavement structural condition from those measurements can aid in identifying more cost 

effective future construction actions by incorporating structural information in addition to 

surface distresses in to the decisionmaking process. The cost associated with the network-level 

deflection measurements can be justified by the overall economic benefit obtained from optimum

decisions that consider structural condition in addition to surface conditions.

In most situations, pavement deflection measurements are performed at the project level as a 

basis to develop design specifications for the most appropriate construction treatment. 

TRAFFIC LOADS

Pavement structures are designed to endure heavy truck loads. Since pavement damage has been 

shown to follow a fourth power exponential relationship to the magnitude of wheel/axle load, 

some measure of the upper 20 percent of applied heavy axle loads can account for up to 

80 percent of the predicted damage potential of those loads. The truck load damage exponent can 
35

also be as low as 2.5 depending on the distress type. It is also possible that one truck overload 

can destroy a pavement structure in one pass. This is why most highway agencies have imposed 

truck axle load restrictions and use full-time load measurement scales to enforce regulations. 

In order to accurately predict future pavement performance, engineers need to know actual heavy 

loads being applied to a pavement. Access to permitted overloads granted by other divisions 

within an SHA is important in determining the need for future construction intervention events. 

At the network level, research has shown that measuring the type of trucks on a route is more 

important than the weight of the trucks. This is based on the observation that truck weights are 

determined by the type of truck. Within a localized region, a typical heavy load truck profile 

can be applied to trucks in the same classification with an acceptable level of uncertainty.

CLIMATE

Climate data are at best a second-order consideration in the planning of future construction 

needs. These data measure potential climate-related “loads” such as freeze-thaw cycles and 

temperature-induced stresses that a pavement may need to resist based on material properties (as 

affected by climate) and constructed drainage features. Predicting future climate change and 

accounting for it is not currently incorporated in most pavement management models. 

The best source of historical raw climate data in the United States is available from the National 

Climate Data Center (NCDC). In the past, NCDC data users needed to conduct quality data 

checks on the NCDC data to avoid using erroneous data; however, NCDC now provides data that 

have been subjected to quality checks. 

MISSING DATA

All infrastructure management systems require a mechanism to handle missing measurement 

data. One approach is based on the truth-in-data concept in which all data are labeled as observed 

or imputed. Imputation of missing data based on defined and documented methods is acceptable, 

provided imputed quantities are appropriately identified. 

The best practice is to associate a measure of variability with both measured and imputed 

condition state parameters. It is expected that the variability of imputed parameters will be 

greater than those based on measurements.

MEASUREMENT VARIABILITY

A requirement for all input measurement methods is to provide a measure of variability related to 

their use in prediction models. These measures of variability should be propagated through the 

models used to predict future pavement condition states to create a posterior probabilistic-based 

performance curve of future condition state. Variability measures should include repeatability of 

the measurement method, be partitioned by spatial variation in the pavement response, and 

reflect the sensitivity of the prediction model to resulting damage estimates.
36

SAMPLING INTERVALS AND FREQUENCY

Collecting pavement condition data on a partial sample of a network is used to reduce data 

collection costs. While sensor data such as pavement roughness and rutting are collected 

continuously, distress data are most often obtained on a sample basis. For manual condition 

surveys, field crews travel between selected portions of the roadway on which the distress 

surveys are performed. For semi-automated distress surveys where interpretations of distress are 

made from video images, only a portion of the video image can be interpreted on a sample basis. 

The condition ratings from these samples are then used to represent the condition of a larger 

pavement segment. Findings from recent research on the effect of pavement sampling on errors 

compared to a continuous measurement include the following:

• For a distress data sample size of 10 percent, errors have been found to have a typical 

maximum of 400 percent as compared to continuous data. An example of a 10 percent

sample size would be rating 0.0621 lane-mi (0.1 lane-km) for every lane-mi (lane-km)

of road.

• A 60 percent sample size reduces the error to approximately ±25 percent.

(9)

• The error due to sample size increases for pavements with higher variability.

Most agencies use monitoring frequencies of 1, 2, or 3 years between pavement condition 

measurements. For cracking, 1-year frequencies appear to be the best so that the first appearance 

of cracking can be detected in order to fit an appropriate model for cracking prediction. Longer 

intervals for crack monitoring may cause an overestimation of agency costs for pavement repair 

at the network level. Monitoring IRI using automated sensor readings could be on a 1- to 2-year 

interval, although longer intervals will result in an underestimation of repair costs at the 

network level.

(9)
37

CHAPTER 8. STRATEGY SELECTION

INTRODUCTION

In order to select the most appropriate corrective pavement construction strategies, many 

considerations must be taken into account, beginning with the pavement condition subject to 

other constraints such as budget, bridge height clearance, guard rail adjustment, buried utilities, 

etc. At the network level, the objective is to characterize the current and future condition state of 

pavements included in the system that require corrective treatments. At the project level, the 

objective is to provide detailed decisions on what corrective construction treatments are needed 

for each project identified from the network-level analysis.

The current challenge to SHAs is to create a rational basis to move from a worst-first to a bestfirst allocation of available agency repair resources. Research has shown that application of 

corrective construction treatments at the proper time can extend the time until more costly 

treatments are required. These types of treatments are applied before too much damage has 

accumulated; hence, the pavements are not in the worst condition. To find these favorable

pavement conditions at the optimum time requires knowledge of how a pavement will respond to 

the prescribed treatment plan based on its current condition. 

There are also situations within a pavement network where pavements in a worse condition 

state should be allowed to continue to deteriorate. This decision is appropriate for pavements 

that have accumulated significant structural damage but are still smooth and provide adequate 

service otherwise. 

NETWORK ANALYSIS

At the network level, the objective is to characterize the current condition state of pavements 

included in the system that require corrective treatments. Since network-level data tend to be 

more aggregated and less specific, the performance models used to predict performance can have 

greater variability. Likewise, strategy selection may be broken down into broad categories using 

a standard set of cost assumptions based on the treatment type. 

Recommended practice at the network level is to use LCC concepts as a means to optimize the 

selection of construction projects in the next cycle and forecast future construction needs. These 

concepts are based on considering multiple streams of future construction activities driven by 

the prediction of future changes in pavements. Optimization is an objective function whose 

performance is based on measured field conditions and main factors that are sensitive to future 

construction activities. In a simple system, optimization is achieved when the best performance 

of the system is obtained using the available budget. 

The pavement performance concept considers the time-history of a chosen functional aspect of a 

pavement. Serviceability has been used in the past where scale is used—the lower the number,

the worse the pavement condition. Using this type of scale, the objective function could 

maximize the area under of the serviceability-time history curve. If a numeric such as IRI is 

used, then optimization of the objective function should focus on minimizing the area under the 
38

IRI-time curve since IRI uses an increasing scale where higher numbers indicate pavements in 

worse condition. 

The limitations of construction strategy selection at the network level are determined by the 

sophistication of the system processes. It is not uncommon that the construction cost assumptions 

used in the network optimization planning process do not match those developed from projectlevel design considerations. Continual improvements are being made to field data collection 

techniques, construction cost models, and new performance prediction curves. Modern 

automated field data collection systems are capable of providing greater amounts of data 

with increased specificity that approaches what used to be considered project-level data. This 

increased data availability requires updates to the construction triggers, threshold limits, and 

performance curves used by SHAs in their network-level strategy selection process.

Strategy selection at the network level are primarily determined by computer algorithms capable 

of accounting for all of the factors included in the simulation of future pavement condition states 

and performing the millions of calculations for impact of alternative strategy scenarios needed 

for optimization of available budgets. 

PROJECT ANALYSIS

Project-level analysis is based on indepth considerations of pavement conditions after a networklevel planning process has identified a pavement segment in need of corrective construction. The 

first level consideration determines the construction need as well as maintenance, restoration, 

resurfacing, rehabilitation, or reconstruction needs. The most significant difference between 

network- and project-level strategy selection is that project-level decisions are based on human 

interpretations of available data and information as opposed to reliance on automated 

computer algorithms. 

At the project level, LCCA can be supplemented with cost engineering considerations specific to 

each project site. Cost engineering concepts require more intensive inputs on the resulting affects 

of alternative design considerations than those generally available at the network level. This level 

of consideration is based on a cost-benefit analysis of alternative construction treatments 

centered on a greater range of treatment options.

Project construction programming is the last chance to integrate other related infrastructure 

construction needs into managing pavement assets. In urban areas, the timing of new utility cuts 

into a pavement structure should be coordinated with repaving events. Having a utility company 

perform changes to a pavement structure less than 3 years since the last construction event is 

often an indication of lack of coordination between publically funded city/regional departments. 

Studies on the impact of the repair of utility cuts in pavements have shown that they return the 

pavement to less than as new condition and can advance deterioration of the surrounding 

pavement structure.

(10)
39

CHAPTER 9. ASSESSMENT AND UPDATE

INTRODUCTION

Modern quality management system concepts are based on a continual cycle of assessments 

and updates. All systems require formal assessments. Based on the results of the assessments, 

updates are performed. 

ASSESSMENTS

Regardless of the age of a management system, formal assessments should be performed at 

periodic intervals to identify improvement opportunities. 

Assessment tactics may include using panels composed of available internal agency staff and

external peer review experts. While internal review panels provide recommendations based on 

the current system, external peer reviews can provide lessons learned from the experience gained 

from other management methods. 

UPDATES

As new materials and construction methods are implemented, the models used for both planning 

and design need to be updated based on field observations. 

Creating a proper database designed to document past construction needs and planned 

construction needs is the recommended approach for updating the various elements included in 

the management system.

41

CHAPTER 10. SUMMARY

In this report, a framework has been proposed to replace the term “life” with a construction 

needs term relative to pavements. The objective of this proposal is to provide a more refined 

definition of future pavement construction needs that is easier to understand. 

The major concepts presented in this report include the following: 

• Current variability of pavement remaining life terminology is confusing.

• Since pavements are repairable systems, life terminology is not appropriate. 

• The use of a terminology based on future corrective construction needs has the potential 

to replace pavement remaining life predictions.

• Prediction of future corrective pavement construction needs are based on the selection of 

construction trigger models that best represent factors used by an agency to schedule 

future treatments, the establishment of thresholds related to condition states of the 

construction triggers, and the development of performance curves that provide a 

prediction of the future change in pavement condition.

• Data collection should be based on providing needed input data to the construction 

trigger models. 

• Network-level strategy selection should be based on LCC considerations and network 

optimization concepts. 

• A continual cycle of improvement updates based on observations of past performance 

and introduction of new technology is required.

The framework concepts were formulated in terms that agencies can use to adapt and enhance 

their current practices.

Appendix A provides a general overview of pavement design and management concepts related 

to pavement life future construction events. Appendix B provides indepth reference citations on 

technical literature related to existing remaining pavement life models. These methods are 

equally applicable to construction needs terminology contained in this document. The 

bibliography contains the literature review summary developed by the project team during 

the project. 

43

APPENDIX A. PAVEMENT DESIGN AND MANAGEMENT CONCEPTS

It is helpful to review pavement engineering design basics in order to develop a common 

terminology to apply to pavement life issues. In this appendix, the basic concepts of pavement 

design and management are reviewed to establish a framework for pavement remaining life 

definitions presented in this report. 

PAVEMENT DESIGN BASICS 

Figure 3 shows the basic concept of modern pavement design. This illustration starts with the 

construction of a new pavement structure. The pavement design life is the predicted time it will 

take for the structure to reach a minimum acceptable condition value. It is important to note that 

all current pavement design methods base the minimum acceptable condition on functional 

service criteria provided by a pavement structure and not an extreme condition that represents 

severe structural defects or prevents vehicle passage.

Figure 3. Graph. Basic concept of modern pavement design.

The definitions of pavement life cycle become more complicated when future pavement 

maintenance, restoration, rehabilitation, resurfacing, and reconstruction events are introduced 

into the design process 

Figure 4 presents pavement design concepts from the 1986 AASHTO Guide for Design of 

Pavement Structures when the initial design life of an alternative pavement trial requires 

consideration of overlays to meet the required performance period or design life.

(11) The 

Serviceability Index (SI) is used to measure pavement conditions. The design period or life is 

shown as time or accumulated traffic. In this example, the required performance period or design 

life exceeds the predicted life of the trial A pavement design. An overlay is required for trial A at 

the time its condition equals the minimum SI. The trial A1 overlay example does not satisfy the 

required performance period, while the trial A2 overlay is expected to exceed the required 

performance period. This is contrasted to the trial B pavement design that exceeds the required 

performance period without need for an overlay. In this case, the life of trial A is required to be 
44

extended with an overlay to achieve the desired performance period because the initial design 

life of trial A is less than required.

Figure 4. Graph. Illustrated service histories of trial pavement designs incorporating future 

overlays.(11)

STAGED CONSTRUCTION AND PERPETUAL PAVEMENTS

The concept of staged pavement construction was introduced to reduce initial pavement 

construction costs with the planned application of a structural overlay early in the life of a 

pavement to extend its life to a desired performance period. While the thickness of the initial 

pavement structure is initially constructed less than required, the added thickness of the overlay 

is applied early enough to meet the initial pavement design life requirements. This concept is 

illustrated in figure 5.
45

Figure 5. Graph. Staged pavement construction design concept.

A perpetual pavement is defined as an asphalt pavement designed and built to last longer than 

50 years without requiring major structural rehabilitation or reconstruction and needing only 

periodic surface renewal in response to distresses confined to the top of the pavement.(12)

The 

basic premise is that an adequately thick asphalt pavement placed on a stable foundation will 

resist distresses that form at the bottom of a pavement structure and are costly to correct. By 

limiting distress formation to the pavement surface layer, the pavement life can be extended in 

perpetuity by rejuvenating the pavement surface materials and remove and replace the type of 

maintenance operation, as illustrated in figure 6. 

Figure 6. Graph. Perpetual pavement design concept based on construction of a pavement 

where distresses occur in the pavement surface layer.
46

MULTIPLE DISTRESS-BASED PAVEMENT DESIGN

Pavement design methods based on the development of multiple pavement distresses, such as the 

MEPDG, use multiple threshold values for each distress considered.

(7) Pavement design life is 

defined as the shortest time it takes for one of the distresses to reach a terminal threshold 

condition. Figure 7 illustrates a hypothetical distress-based pavement design approach consisting 

of pavement roughness and two distress types. In this example, the pavement design life is 

defined by the time that distress 2 reaches the maximum tolerable threshold. Time to distress 

initiation is also illustrated in this figure. The time to distress initiation or first display of a 

distress feature such as cracking can be an important event in the pavement life for maintenance 

and repair planning activities and used to define maintenance-free time periods. 

Figure 7. Graph. Multiple distress-based pavement design where one of the distresses 

reaches a maximum threshold limit. 

REHABILITATION AND RECONSTRUCTION DESIGN

Selecting appropriate corrective treatments to restore the serviceability of a pavement depends 

on its condition at the time of application. As a pavement deteriorates, there comes a point 

when less costly rehabilitation treatments should not be applied, and the pavement must be 

reconstructed. Maintenance, repair, and preservation treatments have a tendency to be more cost 

effective if applied early in the life of a pavement before distresses cause damage to a pavement 

structure, which requires more costly rehabilitation treatments. 

Figure 8 illustrates the general concept of application of corrective pavement treatments as a 

function of pavement condition. Unlike the other time-history pavement condition concept 

figures, the shape of the curve in this figure follows distress progression incorporated into some 
47

popular PMSs. This illustration contains three treatment zones as a function of pavement 

condition. The maintenance, repair, and restoration zone occurs early in the pavement life when 

distresses have not progressed to a state where a rehabilitation option, such as a structural 

overlay, is required. The rehabilitation zone occurs after maintenance, repair, and restoration 

treatments alone are effective and extends to the point where pavement reconstruction is 

required. The wait and reconstruct option represents allowing a pavement structure to deteriorate 

beyond what is considered functionally acceptable.

Figure 8. Graph. Three treatment zones as a function of pavement condition.

PAVEMENT ECONOMICS

LCCA is an engineering economic tool that is useful in comparing the relative merit of 

competing project implementation alternatives. By considering all of the costs (agency and user) 

incurred during the service life of an asset, this analytical process helps transportation officials 

select the lowest cost option or, more commonly, make tradeoff decisions. Additionally, 

LCCA introduces a structured methodology that accounts for the effects of agency activities on 

transportation users and provides a means to balance those effects with the construction, 

rehabilitation, and preservation needs of the system itself.(13) LCCA is the preferred approach 

used to determine pavement type choice and timing of preservation, rehabilitation, and 

reconstruction treatments. 

Agency costs to construct and maintain pavement structures are, in practice, still the primary 

consideration in the pavement management process. Figure 9 illustrates the conceptual tradeoffs 

between pavement resistive capacity, construction costs, and maintenance, repair, and restoration 

costs that can be generated using LCCA. In this illustration, costs are expressed in total LCCs. 

Construction costs increase to provide a pavement structure with greater resistance to applied 

structural and environmental loads. For example, it costs more to build a thicker pavement 

structure to resist structural wheel loads and with better materials to resist environmental 
48

degradation. As the resistive capacity of the initial pavement structure increases, maintenance, 

repair, and restoration costs decrease. For a single pavement structure, a conceptual optimum 

pavement design exists at the minimum total pavement LCC. Under-designed pavements 

typically have a higher maintenance, rehabilitation, and repair cost ratio to construction cost 

than over-designed pavement structures. 

Figure 9. Graph. Conceptual tradeoffs among pavement resistive capacity, construction 

costs, and maintenance, repair, and restoration costs.

Figure 10 illustrates the economic concept of application of corrective pavement treatments. This 

concept is based on pavement deterioration rates accelerating as the pavement accumulates more 

damage. Because of the accelerated deterioration rate, the cost of deferring a treatment also 

increases at an accelerated rate.(14)
49

Figure 10. Graph. Concept of increasing repair cost as a function of pavement 

deterioration.(14)

SUMMARY

The following list summarizes pavement design, rehabilitation, and reconstruction concepts:

• Pavement design is based on providing a pavement that satisfies a functional service 

requirement relative to the role of the pavement structure in the transportation system. 

• Application of maintenance, repair, preservation, and rehabilitation treatments can be 

used to extend the service provided by a pavement past the initial design life. Engineers 

implicitly or explicitly consider these actions during initial design stages.

• Some pavement design concepts are based on construction of a pavement structure so 

that renewal of the upper surface layer can provide a pavement structure that provides 

adequate serviceability as long as the renewal treatments are provided as needed.

• The initial design life of distress specific pavement design methods are based on one of 

the distresses reaching a terminal limiting threshold.

• Pavement condition thresholds are used to indicate when pavement rehabilitation 

treatments or reconstruction are predicted to be needed. 

• A theoretical optimum mix of pavement construction, maintenance, and rehabilitation 

alternatives exists for each pavement structure.

51

APPENDIX B. PAVEMENT RSL PREDICTION MODELS

INTRODUCTION

This appendix presents a summary of major concepts from a literature review of pavement RSL

and other industries related to life and product reliability. In the interest of brevity, equations for 

the various models and statistical mathematical forms have been left out of this review. Those 

interested in mathematical formulations can reference the citations contained in this report. 

CATEGORIZATION OF RSL MODELS

The discussion of RSL prediction models requires a categorization scheme that groups 

models with similar features. The following methods have been used to categorize RSL 

prediction models:

• Witczak classified RSL estimation processes into the following categories: (1) functional 

failure-based approach, (2) structural failure-based approach, and (3) functional and 

structural failure-based approaches.(15)

• Yu used a primary category of mechanical/empirical analyses to classify RSL models. 

Secondary classifications using mechanical analyses included fatigue tests, punch out, 

and falling weight deflectometer (FWD) categories. Secondary classification using

empirical analyses included regression, neural networks, nomographs, life table, 

Kaplan-Meier, failure time theory, and Cox PHs models.(16)

• Vepa et al. used functional failure-based and structural failure-based approaches as a 

primary classification to introduce survivor curve development.(17)

Prediction model classification schemes can also be based on the type of performance model. 

Empirical models are primarily founded on statistical approaches, while mechanistic-based 

models are primarily founded on engineering principles. These categories are not mutually 

exclusive; however, most of the mechanistic-based models use statistical methods for calibration, 

and some of the empirical models incorporate engineering principles.

LIFE TABLE SURVIVOR CURVES

The earliest known survival analysis for pavements in the United States was performed by 

Winfrey and Farrell.(18) The terminology used in the early years of this work was referenced to 

the life of the pavement surface. Survival curves were developed for pavements built each year 

from 1903 to 1937 in 46 States using the life table method. The distribution of survival times is 

divided into year or half-year intervals. For each interval, the mileage of pavement sections that 

were still in service in the beginning of the respective interval, the mileage of pavement sections 

that were out of service at the end of the respective interval, and the mileage of pavement 

sections that were lost (e.g., a road was abandoned) in the respective interval were counted. The 

survival probability of each interval was calculated by dividing the remaining mileage by the 

total mileage in the respective interval. A survival curve was formed by the probability versus 

time interval graph. RSL was estimated by extrapolating the survival curve to zero percent 
52

survival. The past use of the life table method for pavement RSL in the late 1940s, 1950s, and 

1960s was documented.

(19–21)

These are pure empirical models that apply to the inference space in which they were developed. 

They are not representative of changes in construction techniques, materials, traffic loads, or 

definitions of out-of-service pavements.

KAPLAN-MEIER SURVIVAL ANALYSIS

The Kaplan-Meier survival analysis method, also known as the product limit estimator method, 

is a statistical technique used to generate tables and plots of survivor or hazard functions for 

time-to-event data.(22) Advantages of the method are that it accounts for censored data, losses 

from the sample, and non-uniform time intervals between observations. The method assumes 

that events are dependent only on time. Since the method cannot differentiate between the life

of a thin pavement with high traffic and a thick pavement with low traffic, pavements must be 

grouped into families that have similar characteristics, traffic loadings, and environments. In 

essence, a separate survivor curve has to be generated for each factor of interest. The method is 

incorporated in many popular statistical analysis packages and can provide a useful summary of 

available data in exploratory stages of research.

FAILURE TIME THEORY

The failure time theory has been used to develop survivor curves for pavements.(23,24) The basis 

of the failure time theory requires that the underlying functional form of the parametric failure 

distribution be assumed a priori. This allows for the estimation of the coefficients of those 

parameters, which in effect dictate the influential factors. However, this may not be 

feasible when the underlying functional form does not match any known parametric 

statistical distribution. 

COX PH MODEL

The Cox PH model has been widely used in clinical trials to analyze the survival probability 

for patients after a treatment. The median survival time, which is defined as the time when 

50 percent of the subjects will fail to maintain a specified physical condition, is often of interest. 

The distinctive feature of the method is that the ratio of the instantaneous risks of failure (i.e., the 

hazard ratio) at time t for any two given patients in the study does not change with time. The 

advantages of this method are that it does not require that the underlying survival distribution 

be known and the effects of influential factors on survival time can be estimated. A pavement 

is similar to a clinical trial in that after a period of time, it may fail to provide required 

serviceability after a treatment. A pavement is considered to have reached the end of its

useable life if it is rehabilitated or if its condition falls below a specified criterion.(16)

This method has been used for life prediction in many areas of infrastructure management. 

Yu developed a Cox PH model for pavements in Ohio using the Ohio pavement condition rating. 

The rating is based on a 0 to 100 scale where 0 is poor and 100 is excellent. A score of 70 

represents a failure condition. Some of the inferences implied by the models developed on this 

project are counterintuitive. The models are only applicable to the method ODOT uses to rate the 
53

condition of their pavements, traffic, and environmental conditions in Ohio as well as other 

attributes of Ohio pavements. 

1986 AND 1993 AASHTO PAVEMENT DESIGN GUIDES

In the 1986 and 1993 versions of the AASHTO Guide for Design of Pavement Structures, 

pavement remaining life terminology is used in the overlay design methodology.(11,5) In the 1986 

guide, two remaining life estimates are required for the analysis—the remaining life of the 

pavement prior to overlay and remaining life of the overlaid pavement when it reaches its 

terminal serviceability condition.(11) These remaining life values are expressed in terms of a 

percentage and are used to compute a remaining life factor. The remaining life factor is used to 

discount the effective structural capacity of the pavement prior to overlay in order to determine 

the structural capacity needs of the overlay. The 1993 guide uses a similar approach where 

remaining life is expressed as a percentage, but its use is simplified to determine the effective 

structural capacity of the existing pavement.

(5) However, what is called pavement remaining life 

in these documents is really a way to estimate damage to the existing pavement structure and 

does not result in an estimate of the time until a terminal serviceability level is reached. 

While the 1986 and 1993 versions of the AASHTO Guide for Design of Pavement Structures do 

not contain a procedure to estimate pavement life in terms of time until the next rehabilitation or 

reconstruction treatment is required, the design equations used in the methods can be used for 

this purpose.(11,5) These methods use two basic empirical design equations that relate the number 

of traffic loadings (expressed in terms of 18-kip (40-kN) equivalent single-axle loads (ESALs))

to pavement structural capacity, subgrade support properties, pavement serviceability changes, 

and reliability considerations. Estimating the time in years to the specific level of serviceability 

only requires inputs on the pavement structure layer types and thickness, subgrade properties, 

18-kip (40-kN) ESAL applications to date, and the future rate of 18-kip (40-kN) ESAL 

applications. Using the design equations, the number of total applications the pavement structure 

can support until reaching the terminal serviceability level of interest is determined. Subtracting 

the total ESAL applied to the pavement from the total the pavement will support provides the 

remaining ESAL loadings until the terminal serviceability is reached. Dividing this by the 

ESAL rate per year provides a time estimate. The time to rehabilitation or reconstruction can be 

simulated by changing the terminal serviceability level. This is the approach that was previously 

used by the HPMS models as described in the next section of this appendix. 

One of the more severe issues with using the older AASHTO pavement models for pavement 

remaining life analysis is that they are pavement design equations, which are not necessarily 

created as performance prediction models. Further, the models incorporated in the 1986 and 

1993 AASHTO guides can be traced back to the AASHO Road Test.(11,5,4)

The Road Test data 

inference space is severely limited. 

HPMS/HERS MODELS

The remaining life models used for the HPMS analytical process and HERS model are changing. 

The initial models were based on the 1972 AAHSTO Interim Guide for Design of Pavement 

Structures equations.(8) This required pavement condition is expressed in terms of PSR and 

pavement structure capacity as SN for flexible pavements and slab thickness for PCC pavements.
54

Using this system, resurfacing or reconstruction was indicated by the level of PSR. When the 

pavement PSR in an analysis cycle dropped below a minimum tolerable condition based on 

highway functional classification, resurfacing was indicated. Reconstruction was triggered if the 

PSR dropped below the reconstruction threshold. The default minimum tolerable condition and 

reconstruction PSR values are shown in table 2.

(25) For some rural facilities, the average daily 

traffic (ADT) was also used to discriminate the minimum tolerable condition, with lower volume 

facilities having lower trigger points. In the late 1980s data submittal requirements for pavement 

roughness measurements reported as IRI was added. 

Table 2. PSR threshold values used in the HPMS analytical process for minimum tolerable 

conditions for overlay and reconstruction. 

Location Facility Type

Minimum Tolerable 

Condition (PSR)

Reconstruction 

PSR

Rural

Interstate 3.0 2.0

Other principal arterial 3.0/2.8 (6,000 ADT) 2.0

Minor arterial 2.4 1.5

Major collector 2.0 1.1

Minor collector 1.8 0.8

Urban

Interstate 3.2 2.2

Other freeway and expressway 3.0 2.0

Other principal arterial 2.8 1.8

Minor arterial 2.4 1.1

Collector 2.0 1.0

One of the outcomes of the reassessment of the HPMS in 2006 was the development of a new 

data model based on inputs related to the models used in the MEPDG.(7) Pavement-related data 

requirements were expanded and include the following:

(26)

• IRI (annual cycle on NHS, 2 year cycle on non-NHS roadways).

• PSR (2-year cycle; no change).

• Rutting (2-year cycle).

• Faulting (2-year cycle).

• Fatigue cracking (2-year cycle).

• Transverse cracking (2-year cycle).

• Surface type.

• Year of last improvement.

• Year of last construction.

• Last overlay thickness (default values allowed).
55

• Flexible thickness (default values allowed).

• Rigid thickness (default values allowed).

• Base type (default values allowed).

• Base thickness (default values allowed).

• Soil type.

• Climate zone.

The development of simplified models for HERS and NAPCOM was reported by FHWA in 

2007.(27) The objective of this work was to develop simplified models based on the MEPDG 

that could be used with HERS using HPMS data. The definition of RSL for this project is the 

time in age or traffic applications from initial construction or reconstruction to the first major 

rehabilitation. The following pavement distress prediction models were reported to be 

under development:

• JPCP.

o Transverse cracking.

o Joint faulting.

o Joint spalling.

o Pavement smoothness—IRI.

• HMA and HMA overlay pavements.

o Alligator cracking.

o Wheel path rutting.

o Transverse cracking.

o Pavement smoothness—IRI.

• Composite pavement.

o Transverse reflection cracking. 

A key concept in applying these models was to adjust the predictions from the MEPDG design 

models to current observations contained in the HPMS dataset. It appears that the magnitudes of 

the predicted distress level from the model were adjusted to the field observations, and future 

predictions were based on the rate of increase according to pavement age. 
56

Recently, FHWA has developed a PHT analysis tool for HERS and NAPCOM purposes that 

uses HPMS data.(28) Models based on use of the default level 3 MEPDG inputs along with the 

HPMS data are used to predict changes in the multiple pavement condition measures adjusted for 

current observed levels. In this application, pavement health is defined as the time in age or load 

applications from initial construction or reconstruction to the first major rehabilitation as 

warranted by pavement ride and structural conditions. The following distress prediction 

models are included in the tool:

• New HMA and HMA overlay of existing HMA.

o Alligator cracking.

o Rutting.

o Transverse cracking.

o Smoothness—IRI.

• New JPCC.

o Transverse cracking.

o Transverse joint faulting.

o Smoothness—IRI.

• Composite—HMA overlay of existing PCC.

o Reflection cracking.

o Smoothness—IRI.

OTHER CONTEMPORARY PAVEMENT RSL MODELS

The following examples highlight contemporary RSL models developed within the pavement 

engineering community:

• George developed a graphical procedure to determine RSL based on the effective 

thickness ratio derived from nondestructive deflection testing.(29)

• Mamlouk et al. computed RSL based on a fatigue model (considering the rate of crack 

development in Arizona) in conjunction with the backcalculated moduli.(30)

• Huang used two general mathematical distress models to determine the remaining life of 

flexible pavements.(31)

• Park and Kim and Werkmeister and Alabaster developed RSL models based on FWD 

measurements.(32,33)
57

• Santha et al. developed a simple, mechanistic rut-depth prediction model that, when used 

with estimated current traffic, yields the RSL.(34)

• Ferregut et al. and Abdallah et al. applied artificial neural network techniques to develop 

algorithms that combine the functional condition of a pavement (i.e., percent cracking 

and depth of rut) at the time of FWD testing with simple remaining life algorithms to 

predict the remaining life of pavements.(35,36)

• Zaghloul and Elfino used backcalculated layer moduli and expected traffic volumes to 

estimate the RSL of homogeneous sections.(37)

• Gedafa proposed sigmoidal RSL models that can predict flexible pavement RSL based on 

the center deflection from FWD or rolling wheel deflectometer and also take into account 

pavement factors such as layer thickness, traffic, distress, and structural data.(38)

Many of the approaches discussed have great potential to be used at the project level. Due to the 

mechanistic approach of most of these contemporary models, they also have potential to be used 

for public-private partnership-type projects as the results are scientifically based and are likely 

defensible from both the agency and concessionaire perspectives. Used with a cost estimating 

model, these methods could be used to determine the remaining value of a given project.

Many mechanistic approaches rely on determining the structural response of a pavement from 

the various devices that measure the deflection of a pavement surface under various types of 

applied loads. The Long-Term Pavement Performance (LTPP) program has shown that 

deflection measurements possess seasonal variability in the measured responses that can 

introduce uncertainty if corrections are not made for these effects. By definition, preservation 

treatments do not add structural strength to the pavement section. As a result, deflection 

measurements do not account for the increase in service life provided by preventative treatments. 

NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM (NCHRP)

PROJECT 08-71

NCHRP Project 08-71, Methodology for Estimating Life Expectancies of Highway Assets, began 

in July 2009.(39) The objectives of this research were to develop a methodology for determining 

the life expectancies of major types of highway system assets for use in LCCA supporting 

management decisionmaking; demonstrate the methodology’s use for at least three asset classes, 

including pavement or bridges and two others, such as culverts, signs, or signals; and develop a 

guidebook and resources for use by State transportation departments and others for applying the 

methodology to develop highway maintenance and preservation programs and assess the impact 

of such programs on system performance. 

Pavement was one of the assets studied under this project. The following information concerning 

pavement life expectancy was obtained from NCHRP Report 713: Estimating Life Expectancies 

of Highway Assets, Volume II: Final Report.

(40) Traffic loading in particular has been studied 

with field tests for trucks with various suspensions for both static and dynamic loads. Traffic 

loading is considered a better indicator of service life than age, although there is a correlation 

between the two factors; reliability curves built on traffic loading are often used to predict 
58

service life. Other than traffic loading, the amount of distress is primarily utilized. To determine 

the most influential distress type, studies have used a discriminate analysis approach. Depending 

on the pavement type (e.g., asphaltic, concrete, gravel, etc.), additional factors affect the 

life expectancy.

For pavements, factors that affect life expectancy include surface type (i.e., rigid, flexible, and 

composite), design and construction features, traffic loading, climate, age, frequency, and 

intensity of pavement M&R. For each surface type, the project will consider the different

pavement subtypes and thicknesses. The influence of traffic loading will be investigated on the 

basis of the load spectra. Also, literature will be reviewed on the influence of vehicle dynamics 

to gain information on the expectations from the analyses of life expectancy sensitivity to 

operations. The impact of climatic severity on pavement life expectancy will be expressed in 

terms of variables such as freeze index, average temperature, and the number of freeze-thaw 

transitions. The incorporation of M&R activities will be done by determining the impact of 

specific M&R treatments on life extension and determining the impact of different M&R annual 

expenditures (cost per lane-mile) on life extension. The final selection and analysis of influential 

factor sensitivities will be guided by the availability of data. Methods for assessing the 

sensitivities will include the Cox PH model.

FRICTION 

The forecasting of friction is typically performed as a function of environmental variables in an 

effort to predict skid resistance throughout changing weather conditions and seasons. There are 

many models that currently accomplish this goal, but they do not forecast long-term conditions. 

In order to incorporate surface friction characteristics into a pavement life discussion, the focus 

must be on the long-term trend in friction.

Several models have been proposed to predict future skid resistance-based on factors such as 

material properties, traffic loading, and age. Using data from Toronto highways, Emery 

developed an equation in 1982 based on Marshall stability, Marshall flow, mix air voids, 

and a commercial vehicle equivalency factor.(41)

The Wisconsin Department of Transportation developed models in 1996 that predict friction 

number (FN) at 40 mi/h (64 km/h) based on aggregate properties and traffic characteristics 

for HMA and PCC surfaced pavements.

(42) Independent variables include percent dolomite 

in the coarse aggregate, Los Angeles wear rate, accumulated vehicles passes, and percent 

heavy vehicles. 

A 2006 study using Maryland SHA skid data suggests a much simpler prediction model based 

on the age of the pavement.(43) The authors show that FN at 40 mi/h (64 km/h) decreases 

approximately 0.22 FN per year on rural roads and 0.26 FN per year on urban roads. These rates 

are valid only after an initial period of high friction loss (approximately the first year).

The development of pavement age/traffic application friction models were reported by 

Ahammed and Tighe in 2009.(44) Two models each for AC and PCC surfaced pavements were 

developed based on either pavement age or cumulative traffic passes. These models are based on 

LTPP data, which has a much broader geographic scope than many of the other models, which 
59

are typically based on local/regional datasets. These models use a speed term to predict skid 

number at different speeds instead of modeling skid number at just one speed. A unique 

observation modeled in this work is that pavement friction will essentially reach a steady state 

and not continue to degrade with more traffic or age. Although not incorporated into the models, 

the authors also suggest that friction will start to increase as the pavement reaches old age due to 

degradation of the pavement surface through mechanisms such as raveling on AC pavements.

Synthesis reports in 2000 and 2005 document agency practice with respect to acceptable friction 

values.(45,46) Agencies tend to have FN threshold limits between 20 and 30, but those limits are 

typically accompanied by other factors such as crash history or a known friction problem.

The friction threshold in current practice is for maintenance activity, not rehabilitation or 

reconstruction. Treatments such as diamond grinding, open-graded friction course, chip seal, 

or simply posting signage to indicate a low friction area are often used. However, there is no 

indication that any agency currently uses surface friction characteristics as a part of pavement 

life determination.

NOISE

The goal of noise prediction is to predict noise at some location away from the roadway given a 

particular noise level at that roadway. None of the major models attempt to predict noise at some 

future point in time, as it is not considered a necessary factor in determining pavement life.

Similar to friction, there is no indication that agencies use noise as a factor in determining 

pavement life, and the actions taken in cases of excess noise are not rehabilitative in nature. 

Surface treatments such as grinding, grooving, and thin overlays of various types are used to 

improve noise characteristics along with external remedies such as sound barriers and other 

roadside design features.(47,48)

CONCEPTS FROM OUTSIDE THE PAVEMENT INDUSTRY

The body of literature from outside the field of pavements provides a rich source of information 

on theoretical models and terminology that are relevant to pavements. The following list 

provides some key concepts captured by the study team from a review of available information 

outside the field of pavements: 

• A repairable system can be restored to satisfactory operation by any action, including 

parts replacements or changes to adjustable settings. Failure rates and hazard rates only

refer to the first failure times for a population of non-repairable components.(49)

• In a non-repairable population, individual items that fail are removed permanently from 

the population. While the system may be repaired by replacing failed units from either a 

similar or a different population, the members of the original population dwindle over 

time until all have eventually failed.

• Lifetime distribution models are theoretical population models used to describe unit 

lifetimes. The population is generally considered to be all possible unit lifetimes for all 
60

units that could be manufactured based on a particular design, choice of materials, and 

manufacturing process.

• Alternate types of probability density functions (PDFs) are used to describe lifetime 

distribution models.

• A cumulative distribution function is the probability that a unit will fail between times 

defined by the PDF. It is the integral, or area under the PDF curve, between time events 

in the PDF.

• The reliability function or survivability function are defined by the probability that a unit 

survives beyond a specified time. The general rule is to calculate the reliability of a 

system of independent components and multiply the reliability functions of all the 

components together.

• Failure rate is defined for non-repairable populations as the (instantaneous) rate of failure 

for the survivors to time during the next instant of time. The failure rate is sometimes 

called a conditional failure rate since the population of survivors used in the denominator 

converts the expression into a conditional rate, given survival past time.

• The concept of virtual age accounts for the effect of the repair strategy on future system 

performance modeling. If a repair returns a system back to an initial state of performance, 

the virtual age of the system is reset to zero. At the other extreme are minimal repairs that

have no impact on future performance of the system and the virtual age is equal to the 

actual age of the system.(50)

For many years and across a wide variety of mechanical and electronic components and systems, 

empirical population failure rates have been calculated as units of age over time and repeatedly 

obtained the “bathtub curve,” which illustrates instantaneous failures rates over a product’s life. 

The bathtub curve concept is depicted in figure 11 and has three distinctive time periods: the 

early life or infant mortality period, constant failure rate or useful life period, and end-of-life or 

wear out period. The high rates of failure during the early life or infant mortality period are 

characteristics of weak units with manufacturing or other defects. The early period is followed 

by a period of relatively constant failure rate also known as the intrinsic failure, normal, or useful 

life period. During this period, failures tend to be more random in nature due to various effects 

that impact life depending on the type of component. This is followed by the wear out period 

when the failure rate increases at the end of the product life. 
61

Figure 11. Graph. Classical bathtub curve of component failure rate versus time. 

The bathtub curve is expressed as a function of failure rate. Failure rate is expressed in units of 

failure per component-time. Standards for the mean time between failures (MTBF) statistic are 

defined as the reciprocal of the failure rate during the constant rate failure period when the 

failure rate is a minimum value.

(51) In many interpretations, the component part of the statistics is 

not stated and the units are expressed as time/failure, which can be deceiving since it has no 

direct relationship to the life of a product. For example, it is possible for a product to have a 

MTBF exceeding 100 years since it is based on the minimum failure rate but have a life 

expectancy of less than 10 years based on the time until the electrical component or system 

actually fails. Although MTBF is useful for relative comparison of different components or

devices, it is not an indicator of expected life.

A comprehensive and continuously updated source of information the project team found comes 

from the National Institute of Standards and Technology and Semiconductor Manufacturing 

Technology NIST/SEMATECH e-Handbook of Statistical Methods.

(49) The following list 

contains information on the various concepts and models from this handbook:

• Repair rate models are based on counting the cumulative number of failures over time. 

Time is measured by system power-on hours from initial turn-on at time zero to the end 

of system life. Failures occur at given system ages, and the system is repaired to a state 

that may be the same as new, better, or worse.

• The repair rate or rate of occurrence of failures is the mean rate of failures per unit time

or the first derivative of average or expected number of failures for each time segment.

• Acceleration models predict time to failure as a function of stress. Acceleration factors 

show how time-to-fail at a particular operating stress level (for one failure mode or 
62

mechanism) can be used to predict the equivalent time to fail at a different operating 

stress level. Acceleration models are usually based on the physics or chemistry 

underlying a particular failure mechanism. Successful empirical models often turn out to 

be approximations of complicated physics or kinetics models when the theory of the 

failure mechanism is better understood. Some acceleration models are as follows:

o Arrhenius model predicts failure acceleration due to temperature increase.

o The Eyring model has a theoretical basis in chemistry and quantum mechanics and 

can be used to model acceleration when many stresses are involved.

o The Coffin-Manson model is a useful non-Eyring model for crack growth or 

material fatigue.

• The following parametric models have successfully served as population models for 

failure times arising from a wide range of products and failure mechanisms. Some models 

are probabilistic arguments based on the physics of the failure mode that tend to justify 

the choice of model. Other models are used solely because of their empirical success in 

fitting actual failure data.

o The exponential model, with only one unknown parameter, is the simplest of all 

life distribution models. The exponential model is used for the flat portion of the 

bathtub curve, where most systems spend most of their lives. Mathematical 

equations can be found in section 8.1.6.1 of the NIST/SEMATECH e-Handbook 

of Statistical Methods.

(49)

o The Weibull model is a very flexible life distribution model with two basic 

parameters that can be increased to three by introducing a waiting time parameter. 

Because of its flexible shape and ability to model a wide range of failure rates, the 

Weibull model has been used successfully in many applications as a purely empirical 

model. The Weibull model can be derived theoretically as a form of extreme value 

distribution, governing the time to occurrence of the weakest link of many competing 

failure processes. Mathematical equations can be found in section 8.1.6.2 of the 

NIST/SEMATECH e-Handbook of Statistical Methods.

(49)

o Extreme value distributions are the limiting distributions for the minimum or the 

maximum of a very large collection of random observations from the same arbitrary 

distribution. In the context of reliability modeling, extreme value distributions for the 

minimum are frequently encountered. The extreme value distribution is useful for 

modeling applications for which the variable of interest is the minimum of many 

random factors, all of which can take positive or negative values. Mathematical 

equations can be found in section 8.1.6.3 of the NIST/SEMATECH e-Handbook of 

Statistical Methods.

(49)

o The lognormal life distribution, like the Weibull model, is a very flexible model 

that can empirically fit many types of failure data. The lognormal model can be 

theoretically derived under assumptions matching many failure degradation processes 
63

common to electronic (semiconductor) failure mechanisms. Some of these are 

corrosion, diffusion, migration, crack growth, electromigration, and, in general, 

failures resulting from chemical reactions or processes. Mathematical equations 

can be found in section 8.1.6.4 of the NIST/SEMATECH e-Handbook of 

Statistical Methods.

(49)

o The gamma distribution is a flexible life distribution model that may offer a good fit 

to some sets of failure data. The gamma is used in standby system models and also 

for Bayesian reliability analysis. The chi-square distribution is a special case of the 

gamma distribution. Mathematical equations can be found in section 8.1.6.5 of the 

NIST/SEMATECH e-Handbook of Statistical Methods (49)

o The 1969 Birnbaum and Saunders fatigue life distribution model is based on a 

physical fatigue process where crack growth causes failure. The Birnbaum-Saunders 

assumption, while physically restrictive, is consistent with a deterministic model from 

materials physics known as Miner's Rule or Miner’s Hypothesis. Mathematical 

equations can be found in section 8.1.6.6 of the NIST/SEMATECH e-Handbook of 

Statistical Methods.

(49)

o The Cox PH model has been used primarily in medical testing analyses to model the 

effect of secondary variables on survival. It is more like an acceleration model than a 

specific life distribution model, and its strength lies in its ability to model and test 

many inferences about survival without making any specific assumptions about the 

form of the life distribution model. This type of model was developed to predict the 

remaining life of pavements in Ohio using a PCI type of rating system.(16)

As documented in the literature review performed for this project, many of these concepts and 

statistical distributions have been used in the pavement industry to predict pavement 

performance and time until intervention is required. 

The terminology and statistics related to repairable systems offer a good theoretical basis for 

future development of pavement RSL models. A pavement is a repairable system. The concept of 

virtual age of a system appears to have some applicability to pavements. Some examples of the 

application of the virtual age concept to improvement of pavement models include the following:

• Pothole patching is a minimal repair that does not change the rate of damage 

accumulation along the structure. The virtual age of the pavement system is still 

equal to the actual age after patching of spot surface defects.

• Replacement of the upper surface layer of an AC/HMA pavement designed for top-down 

cracking before the cracks extend too deep into the bound portions of the pavement 

structure is an example of a repair that can return a system to a new condition and reset 

the virtual age of the system to zero in terms of distress prediction models. 

• Mill and overlay repair techniques fall somewhere between the minimal and perfect 

repair scenarios. While these repairs do not return the pavement system to a “good as new 

condition,” they should reduce the virtual life starting point in a RSL model by some 
64

fraction. In other words, while the repair/treatment does not return the pavement to good 

as new status, it should retard the distress rate to an earlier virtual model age. 

• Most pavement reconstruction activities can be considered as repairs since they tend to 

only affect the upper bound portions of a pavement structure. The parts of the pavement 

system related to embankments, base layers, subsurface, and surface drainage features 

tend to not be changed unless they are identified as a significant contributor to the cause 

of pavement degradation.

LITERATURE REVIEW OBSERVATIONS 

The following observations are based on the literature review of pavement RSL models and 

concepts from outside the pavement industry related to the objectives of this project: 

• General empirical population models based on concepts such as survivor curves are 

applicable only to the population on which they are based. The basis for these statistics 

tends to be ill-defined and do not account for changes in pavement technology. One must 

wait more than 10 years for this type of statistical inference base to catch up with 

technology changes.

• Current pavement service life prediction models are by necessity specific to the condition 

measurement system on which they are based. 

• Pavement condition prediction models are specific to the condition and inference space 

for which they were created. This means that a model based on a measurement standard 

used in one jurisdiction is only applicable to agencies that use the same measurement 

standard and have similar types of pavement structures, with similar age, materials, and 

traffic/environmental conditions.

• In this review, pavement surface friction is treated as a defect repairable by maintenance 

types of treatments. Pavement noise prediction models are rarely associated with a 

pavement life history model that associates increased noise as a function of pavement 

structure age. While pavement surface texture can affect noise generation, pavement 

noise is not a first order consideration in the application of pavement treatments.

• Modeling terminology and concepts based on repairable systems from literature appear to 

be a good basis for future developments in pavement condition modeling. The virtual 

system age concept related to the impacts of maintenance, repair, and restoration 

treatments offers a good nomenclature framework to describe the effects of pavement 

corrective treatments.

• Advanced statistical modeling techniques exist for system reliability based on a defined 

numerical measurement system and nomenclature related to repairable systems.

• A common issue in all service life models both within and outside the pavement industry 

is the basis for failure threshold limits. The SI developed at the AASHO Road Test used 

a human panel to rate pavement acceptability.(4) Subjective ratings are known to change 
65

with time, technology, location, visible maintenance features, in-vehicle noise, and other 

conditions. Combined distress indices often use threshold limits whose basis is not 

well documented or hard to find. One study of acceptable road roughness found that 

15 percent of users found roughness levels higher than 170 inches/mi (2.7 m/km) to be 

acceptable, although this is still a suggested threshold value for roughness.

(52)

67

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