Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Pavement Remaining Service Interval

Implementation Guidelines

Publication No. FHWA-HRT-13-050 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 provides 

guidelines for implementing the RSI concept as a replacement to the current remaining life 

terminology for pavements. The RSI concept is broken down into a series of steps that follow a 

logical progression. Examples of the concept are presented using pavement engineering 

methodologies in current use. Suggestions are also provided based on the results of the RSI 

process. While this report focuses on pavements, it is also applicable to other types of 

transportation infrastructure. A companion report presents the framework for replacing the 

current RSL terminology with one based on more exact construction event terms.(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-13-050

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

4. Title and Subtitle

Pavement Remaining Service Interval Implementation Guidelines 

5. Report Date

November 2013

6. Performing Organization Code

7. Author(s)

Gary E. Elkins, Gonzalo R. Rada, Jonathan L. Groeger, and Beth Visintine

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

Implementation Guidelines

October 2009–May 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 provides step-by-step guidelines for implementing the RSI terminology. A 

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

construction event terms.(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 expectancy curves, Pavement data 

collection, Pavement strategy selection, Repairable system

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

62

22. Price

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

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius o

C 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit o

F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS

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

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

BACKGROUND ..................................................................................................................... 3

REFORMULATING RSL TERMINOLOGY ..................................................................... 4

REPORT ORGANIZATION................................................................................................. 5

CHAPTER 2. RSI CONCEPT OVERVIEW............................................................................. 7

CONSTRUCTION NEEDS PROCESS ................................................................................ 7

CONSTRUCTION EVENT TERMINOLOGY................................................................... 8

RSI IMPLEMENTATION FRAMEWORK ...................................................................... 10

CHAPTER 3. IMPLEMENTING THE RSI CONCEPT........................................................ 11

STEP 1—SETTING CONSTRUCTION TRIGGERS ...................................................... 11

STEP 2—SETTING THRESHOLD LIMITS .................................................................... 14

Subjective Approach......................................................................................................... 14

Engineering Approach ...................................................................................................... 14

Empirical Approach.......................................................................................................... 15

Economic Analysis Approach........................................................................................... 15

Combinations Approach ................................................................................................... 16

STEP 3—SELECTING OR DEVELOPING EXPECTANCY PERFORMANCE 

CURVES ................................................................................................................................ 17

Models Based on Design Equations ................................................................................. 18

Empirical Models.............................................................................................................. 20

Agency Time-Based Rules ............................................................................................... 20

STEP 4—IDENTIFYING COLLECTION OF INPUTS .................................................. 21

Pavement Roughness........................................................................................................ 21

Pavement Distress............................................................................................................. 22

Pavement Structural Response.......................................................................................... 22

Traffic Loads..................................................................................................................... 23

Climate.............................................................................................................................. 24

STEP 5—ESTABLISHING A STRATEGY SELECTION PROCESS........................... 25

Network Analysis.............................................................................................................. 26

Project Analysis................................................................................................................ 27

STEP 6—PERFORMING PERIODIC ASSESSMENTS AND UPDATES.................... 27

Assessments...................................................................................................................... 27

Updates ............................................................................................................................. 28

CHAPTER 4. RSI IMPLEMENTATION EXAMPLES......................................................... 29

EXAMPLE 1—SHA USING PHT ANALYSIS TOOL..................................................... 29

Background....................................................................................................................... 29

RSI Implementation.......................................................................................................... 29

EXAMPLE 2—SHA USING THE 1972 AASHTO INTERIM GUIDE FOR DESIGN 

OF PAVEMENT STRUCTURES ......................................................................................... 33

Background....................................................................................................................... 33

RSI Implementation.......................................................................................................... 33
iv

EXAMPLE 3—SHA USING MULTIPLE PERFORMANCE INDICATORS .............. 38

Background....................................................................................................................... 38

RSI Implementation.......................................................................................................... 39

LCC CONSIDERATIONS................................................................................................... 44

CHAPTER 5. COMMUNICATING RSI FRAMEWORK RESULTS ................................. 47

APPENDIX. GENERIC AGENCY ISSUES............................................................................ 49

ESTABLISHING AN RSI PROTOCOL ............................................................................ 49

IDENTIFYING AND APPOINTING AN RSI COORDINATOR ................................... 49

DISSEMINATION OF RSI CONCEPT WITHIN AGENCY.......................................... 50

REFERENCES............................................................................................................................ 51

BIBLIOGRAPHY....................................................................................................................... 53
v

LIST OF FIGURES

Figure 1. Flowchart. Future pavement construction needs process................................................ 7

Figure 2. Flowchart. Agency RSI implementation ....................................................................... 10

Figure 3. Graph. Conceptual relationship between agency repair costs as a function of 

pavement condition....................................................................................................................... 16

Figure 4. Graph. Three treatment zones as a function of pavement condition ............................. 17

Figure 5. Graph. Illustrated service histories of trial pavement designs incorporating future 

overlays......................................................................................................................................... 19

Figure 6. Graph. Multiple distress-based pavement design where one of the distresses 

reaches a maximum threshold limit.............................................................................................. 19

Figure 7. Photo. Inertial road profilers used to measure IRI, RN, and other roughness 

indices........................................................................................................................................... 21

Figure 8. Photo. FWD used for structural evaluation ................................................................... 23

Figure 9. Photo. TSD used for structural evaluation .................................................................... 23

Figure 10. Photo. TSD internal pavement response measurement instrumentation..................... 23

Figure 11. Photo. WIM sensor used to record truck axle and gross vehicle weights................... 24

Figure 12. Photo. Mini site-specific SMP weather station used to record climatic data .............. 24

Figure 13. Equation. Reflection cracking model .......................................................................... 30

Figure 14. Equation. IRI prediction model ................................................................................... 31

Figure 15. Equation. Flexible pavement design equation............................................................. 35

Figure 16. Equation. SN................................................................................................................ 35

Figure 17. Flowchart. Partial set of construction event combinations and schedule of 

activities for example 1................................................................................................................. 46

LIST OF TABLES

Table 1. Reflection cracking or IRI construction triggers-threshold limits.................................. 30

Table 2. Reflective cracking model parameter d .......................................................................... 31

Table 3. Network-level construction trigger-threshold limits....................................................... 34

Table 4. Project-level construction trigger-threshold limits......................................................... 34

Table 5. RSI based on PSR values and construction treatment .................................................... 38

Table 6. Construction trigger-threshold limits matrix .................................................................. 40

Table 7. RSI based on construction treatments............................................................................. 43
vi

LIST OF ABBREVIATIONS

AADT Average annual daily traffic

AASHTO American Association of State Highway and Transportation Officials 

CPR Concrete pavement restoration

FHWA Federal Highway Administration

FN Friction number

FWD Falling weight deflectometer

GPR Ground penetration radar

HMA Hot mix asphalt

HPMS Highway Performance Monitoring System

IRI International Roughness Index

JPCP Jointed plain concrete pavement

LCC Life-cycle cost

MEPDG Mechanistic-Empirical Pavement Design Guide

NCDC National Climate Data Center

PCC Portland cement concrete

PCI Pavement Condition Index

PHT Pavement health track

PMS Pavement management system

PSI Present Serviceability Index

PSR Pavement serviceability rating

RSI Remaining service interval

RSL Remaining service life

RN Ride number

SHA State highway agency

SMP Seasonal monitoring program

SN Structural number

TSD Traffic speed deflectometer

WIM Weigh-in-motion
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 decisionmaking

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 report introduces terminology that removes the word 

“life” from the lexicon. 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 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

BACKGROUND

Many decisions need to be made in order to successfully provide and manage a pavement 

network. The main decision is the prediction of future construction events, which is the 

fundamental basis of engineering design and management of pavement structures. Future 

construction events include the do nothing alternative, routine reactive maintenance, preventive 

maintenance and preservation activities, alternative rehabilitation treatments, and reconstruction. 

The goal of pavement management is to optimize agency resources while providing a maximum 

level of service to users. Accomplishing this goal requires monitoring the condition of the 

pavement network and forecasting future pavement performance in order to effectively plan 

future pavement construction events. Predicting RSL of the segment units that make up the 

pavement network is of paramount importance to pavement management planning. Knowing or 

estimating the future condition of pavement sections is the rational basis of informed pavement 

infrastructure planning.

Remaining service is typically defined as the period over which a pavement section adequately 

performs its desired function or performs to a desired level of service. RSL is the time from 

the present (i.e., today) to when a pavement reaches an unacceptable condition and requires

construction intervention. The central role of RSL in 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 is data requirements related to level of technical detail, extent, quality, 

precision, and accuracy.

While the prediction of time until a construction treatment should be applied is a critical 

component at all levels of pavement management decisions, many issues exist in the current 

RSL terminology, 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; 

however, these are two different events in terms of the condition of the pavement at the time of 

construction as well as the associated construction costs. Rehabilitation treatments are typically 

applied before a pavement has suffered extensive structural damage, while reconstruction 

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

deterioration. Attempting to interpret combined RSL estimates from mixed rehabilitation and 

reconstruction units provides little information to decisionmakers. Also, the timing of the next 

rehabilitation or reconstruction event will depend on what future lower-level treatments 

are applied.

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

This definition not only has the same issues as rehabilitation and reconstruction RSL units, but it

also introduces other service and safety condition indices that further complicate the meaning of 

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

ratings, such as cracking, can be complicated to justify. Moreover, interpretation of a single RSL 
4

number becomes 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 faulty construction decisions since the construction treatment to correct roughness 

may ignore 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, the number of 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 useful 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.

An unintentional consequence of using current RSL terminology is that it tends to promote 

“worst-first” approaches to correcting pavement deficiencies. By expressing pavement condition 

in terms of RSL, it is expected that pavements in the worst condition get treated first; however,

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 include pavements with differing 

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

REFORMULATING RSL TERMINOLOGY

The objective of this document is to provide a definition and process for determining pavement 

RSL that will promote consistency in the use of the terminology. Currently, many RSL 

definitions are used to describe different events in the construction history of a pavement. 

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

management decision process because the primary purpose of predicting pavement RSL 

within the context of pavement management is to plan for future field construction event(s)

(i.e., maintenance, preservation, rehabilitation, reconstruction, or other treatments to correct 

some attribute of the pavement structure). 
5

A major source of uncertainty in the current definition is the use of the term “life” to represent 

different points in the construction history. In the pavement design context, “life” is used to 

represent the time until the as-designed pavement structure reaches an unacceptable condition. 

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 is to remove the word “life” from the lexicon since it is the basis for 

confusion. Instead of using RSL, it is recommended to adopt the term RSI, which indicates the

time remaining until a defined construction treatment is required. 

Adoption of a definition related to construction treatments opens up the vocabulary to treatments 

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

defined in terms of the time until the next construction event requiring 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, or the 

pavement has reached a level where the next utility cuts and resulting repairs can be performed 

within a defined time period. 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 construction is needed. 

REPORT ORGANIZATION

This report provides a more refined construction activity needs analysis terminology and 

approach to reduce potential confusion over the use of the RSL nomenclature. To accomplish 

this task, this report is organized into the following sections:

• Chapter 1 provides background information on the RSL concept and establishes the need 

to reformulate this concept.

• Chapter 2 discusses the basic process for determining future pavement construction 

needs, introduces construction event terminology, covers the framework associated with 

the RSI concept, and introduces the RSI implementation steps.

• Chapter 3 provides a step-by-step description of the process required to establish the RSI 

concept by SHAs, including construction triggers, threshold limits, expectancy curves, 

collection of inputs, strategy selection, and assessments and updates. 

• Chapter 4 presents hypothetical examples to illustrate the RSI implementation process 

described in chapter 3.
6

• Chapter 5 discusses key issues surrounding the results generated within the context of the 

RSI framework.

• The appendix addresses generic issues associated with the implementation of the RSI 

concept within an agency. These issues are not part of the actual RSI implementation 

process, but they are critical to the success of an agency’s migration to the RSI concept. 

These guidelines have been developed based on the results of the effort carried out under the 

Federal Highway Administration (FHWA) project, “Definition and Determination of Remaining 

Service and Structural Life.” These results have been documented in the FHWA publication,

Reformulated Pavement Remaining Service Life Framework.

(1) Users are encouraged to read this 

report prior to implementing the RSI concept, as it provides the foundations for the concept 

along with other valuable information such as basic pavement design and management concepts, 

RSL models, and the results of the literature review performed as part of the project.
7

CHAPTER 2. RSI CONCEPT OVERVIEW

CONSTRUCTION NEEDS PROCESS

The basic process used 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 an 

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

the type of pavement asset. The process starts with input data that are fed into the expectancy 

model to 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 documentation of 

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

Monitoring measurements provide updated inputs for the next planning cycle and also refine 

expectancy models.

Figure 1. Flowchart. Future pavement construction needs process.

Within the context of the process illustrated in figure 1, the fundamental elements required to 

replace the existing RSL terminology with the new RSI terminology include the following:

• A controlled vocabulary to define pavement construction events. 

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

• How future needs are determined to differentiate between different levels of 

business decisions. 

• The location and extent of the needed treatment.
8

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

is needed from how the need is determined.

CONSTRUCTION EVENT TERMINOLOGY

The objective of construction event terminology is to uniquely define what type of predicted 

future construction event is needed. This vocabulary is needed to describe the construction 

treatments to promote database integration and increase levels of aggregation at local, 

district, State, and national levels. Moreover, the terminology requires the identification of 

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 since this is the basis for budget 

planning. 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 considering the design lane 

(which receives the most truck loadings), multilane facilities, damaged lanes (which are the lanes 

in the worst condition), and other local factors that influence pavement damage from vehicle and 

environmental effects.

The following examples highlight definitions of construction events based on the expanded 

paradigm of common pavement improvements included in many pavement management 

systems (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 thickness of bound pavement layers by more than 

25 percent.

• Concrete pavement restoration (CPR): Application of full- or 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 grooves in the surface of a pavement without application of a new 

material layer.
9

• 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.

The definitions only describe what type of construction treatment is being applied to the 

pavement. 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 in terms of 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.

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

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

the agency appropriate for the route.

Typically, an agency will develop a decision matrix to use as part of its pavement management

process. 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.
10

RSI IMPLEMENTATION FRAMEWORK

The framework for implementing the new RSI terminology is illustrated in figure 2. The key 

components of the framework that must be addressed by SHAs to develop, implement, maintain, 

and update a construction needs analysis methodology that includes generic agency and RSI 

implementation issues.

These components should be tailored to the individual agency requirements related to budgeting 

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

construction contract instruments, and other considerations. 

The generic agency issues address the establishment of the agency’s RSI protocol, the 

identification of an RSI coordinator, and the dissemination of the RSI concept within the agency. 

These issues only need to be addressed once, with periodic monitoring and revising to ensure 

they are still appropriate; however, they are vital to the success of an agency’s RSI program. 

These generic agency issues are discussed in more detailed in the appendix. 

Figure 2. Flowchart. Agency RSI implementation.

The RSI implementation issues focus on a step-by-step approach that is necessary to successfully 

establish the RSI concept within the agency. These RSI implementation steps are discussed in

the following two chapters of this report.

1. Construction triggers,

2. Threshold limits,

3. Expectancy curves,

4. Collection of inputs,

5. Strategy selection, and

6. Assessments and updates

1. RSI Protocol

2. RSI Coordinator

3. RSI Dissemination

Generic Agency Issues

RSI Implementation Issues
11

CHAPTER 3. IMPLEMENTING THE RSI CONCEPT

The first step in implementing the RSI concept is deciding that a change is needed in how an 

agency presents pavement construction needs to different departments within the agency and to

decisionmakers as well as understanding the benefits of interagency information exchange. The 

RSI concept is based on the technical aspects of when, what, and why future construction 

treatments are required. Conversely, the RSL concept acts as an overall measure of the system 

life status based on hidden factors not properly expressed. The RSI concept creates a rational 

basis for applying modern risk-based models, which address the variability inherent in current 

condition measurements, future condition predictions, and resulting economic impacts of 

pavement management decisions. This allows for the creation of multifaceted pavement

performance measurements tools that are based on more than simple pavement condition indices. 

It is recommended that SHA personnel responsible for implementing the RSI concept undertake 

the following steps before proceeding with the six implementation steps:

• Review Reformulated Pavement Remaining Service Life Framework.

(1)

• Review the guidelines contained in this document.

• Hold a brainstorming meeting with agency pavements program stakeholders to discuss 

the various RSI elements, issues, and implementation steps. 

This chapter addresses the following six steps to successfully implement the RSI concept by a 

highway agency, including alternative options within each step where applicable:

1. Setting construction triggers.

2. Setting threshold limits.

3. Selecting or developing expectancy performance curves.

4. Identifying collection of inputs.

5. Establishing a strategy selection process.

6. Performing periodic assessments and updates.

The steps are presented in a logical sequence, but this does not imply that they are independent 

of each other. For example, setting construction triggers in step 1 or setting threshold limits in 

step 2 cannot be done independently of selecting the expectancy performance curves in step 3.

STEP 1—SETTING CONSTRUCTION TRIGGERS

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 
12

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

segment. Some considerations in selecting construction triggers include the following:

• Historical practice.

• Related agency practice.

• Extent of the pavement network being managed.

• Data collection budget.

• Types of pavements or family of pavements managed by the agency.

• Required measurement accuracy, precision, and detail.

• Functional class of pavements in the network.

• Common distress manifestations.

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

Construction triggers that may be considered by an individual SHA based on its current or 

planned pavement practices include the following:

• Level of service: These triggers are primarily based on human factor ratings of the 

pavement serviceability and measurement of pavement roughness. The use of pavement 

roughness as a primary indicator of level of service is originally based on the American 

Association of State Highway Officials Road Test conducted in the late 1950s.

(2)

Currently, pavement roughness, expressed in terms of the IRI, is the most common 

measure of pavement level of service.

• Pavement surface distress: 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:

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

deteriorate the quickest and require construction intervention to correct. 

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

o Create or use an existing numerical index based on assigning deducting values for the 

type, extent, and severity of a selected range of distresses.

o Develop a correlation between distresses and level of service indicators.
13

o Associate distress types with corrective treatments (e.g., potholes can 

be corrected with patches, while severe fatigue cracking usually requires 

pavement reconstruction).

• Structural considerations: These considerations are typically based on certain types of 

distress and non-destructive pavement deflection testing. 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. Conversely, deflection measurements can be used as a diagnostic tool to 

look below the pavement surface to get an indication of subsurface damage. Pavement 

structural information from deflection measurements include the following:

o Modulus (stiffness) of the pavement foundation layer (subgrade). 

o Indicators of the relative stiffness of the surface and base layers.

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

o Stiffness characterization of distinct pavement layers.

o Effective structural capacity of pavement based on the 1993 American Association of 

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

Pavement Structures or other methodology.(3)

• Safety aspects: Pavement condition safety aspects are primarily related to friction and 

hydroplane potential. Pavement friction characteristics are most often characterized using 

a skid number parameter. Hydroplane potential is related to ponding of water on the 

pavement surface due to the ability of pavement ruts to hold water or drainage defects 

in the surface profile. The nominal depth of water can be associated with hydroplane 

potential based on the speed limit. Another pavement condition related to safety concerns 

is excessive pavement roughness relative to the speed limit. It is possible for localized 

bumps, dips, faults, and holes in the pavements to reach a level that negatively influences 

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 time between construction events. The time-based rules are intended to reduce 

field measurement costs and provide a proactive pavement management approach to keep 

pavements in good working 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 a roadway needs to be 

expanded due to traffic growth. These types of rules are generally outside the realm of 

pavement management, but they are required when developing an estimate of future 

budget needs.
14

STEP 2—SETTING THRESHOLD LIMITS

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

a corrective or preventive construction treatment is needed. There are two general types of 

threshold limits: one related to road users and one based on agency economics. For example, 

ride quality, rutting, and skid resistance are related to road users, while cracking and faulting 

(although it impacts ride quality) are related to agency economics and should ideally be based 

on LCCs. The specific methods and procedures that can be used to establish the various types 

of threshold limits include subjective, engineering, empirical, economic analysis, and a 

combination, which are described in the following subsections.

Subjective Approach

Subjective approach threshold limits are based on ratings from a panel of judges comprised of 

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:

• 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.

• Five-level Likert scale: The five-level Likert scale provides a measure of the range in 

acceptance criteria to be considered (e.g., 5 = totally acceptable, 4 = slightly acceptable, 

3 = neutral or not sure, 2 = unacceptable, and 1= totally unacceptable). 

• Four-level Likert scale: The four-level Likert scale removes the neutral middle rating to 

force raters to provide either an acceptable or unacceptable rating (e.g., 4 = totally 

acceptable, 3 = slightly acceptable, 2 = unacceptable, and 1= totally not acceptable).

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

ratings (e.g., 5 = very poor, 4 = poor, 3 = fair, 2 = good, and 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. 

Engineering Approach

Considerations used to establish these threshold limits are based on pavement performance 

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

considerations include the following:

• Cracks extend completely through bound pavement layers. 

• Potential depth of water held in ruts. 

• Depth of top-down cracks on asphalt concrete pavements. 
15

• Increase in applied dynamic truck loads caused by pavement roughness. 

• Pavement structural capacity diminished to the point where stress and strain level of 

applied traffic loads accelerates pavement damage. 

Empirical Approach

The empirical approach to setting threshold limits is based on observing events. A critical aspect 

to this approach is that it is most applicable to the inference space of the observations from 

which they were developed. Technology advancements or other changes that 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 setting threshold limits 

include the following:

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

level of friction.

• Statistical analysis of the pavement condition when construction treatments have been 

applied and their effectiveness (as a function of condition) when they were applied.

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

mechanism being modeled. For example, it may be postulated that the accident rate on a section 

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

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

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

understand all of the mechanisms associated with the accident rate; rather, the correlation 

of the friction with the accident rate can assist in identifying an unacceptable level of friction 

on the roadway.

Economic Analysis Approach

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. A critical factor in pavement 

construction time-series economic analysis are the rules concerning deterioration rate of the 

pavement, what type of repair treatments are considered, the effect pavement condition has on 

the resulting performance for each repair treatment, and costs included in the analysis. To avoid 

manipulating the results from pavement LCC analysis, SHAs need to create a set of rules. To 

the extent possible, these rules should be based on observations from pavements under agency 

jurisdiction. However, the use of engineering judgment based on available data may be the best 

option to create the rules. Establishing a preliminary set of rules provides a basis to evaluate and 

update them based on experience. Part of the rules should be standard estimates of error and 

error distribution forms for use in stochastic/risk-based analysis. Figure 3 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.
16

Figure 3. Graph. Conceptual relationship between agency repair costs as a function of 

pavement condition.

Combinations Approach

Under this scenario, the development of threshold limits is done through a combination of the 

approaches previously described. The recommended approach to setting threshold limits on need 

for corrective construction intervention is by using 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. 

Considerations to determine appropriate pavement state thresholds for application of 

maintenance or preservation treatments include the following:

• Preservation maintenance treatments should be applied while a pavement is still in 

relatively good condition. 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 distress-based indices are being used, 

then an expert subjective option can be used to determine a nominal threshold value when 

preservation maintenance activities can be used. Figure 4 illustrates the general concept 

of applying corrective pavement treatments as a function of pavement condition.

• A strategy for setting threshold limits for the lower limit of a pavement condition is by 

considering when the extent and severity of distress types require corrective treatments 

that can be considered as structural improvements. Some consideration 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 The extent of faulting of jointed PCC pavement requires a combination of grinding, 

ultra-thin overlays, and dowel bar retrofit to correct. 
17

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. 

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

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

operating costs. 

Figure 4. Graph. Three treatment zones as a function of pavement condition.

A common rule of thumb is that an overlay thicker than 2 inches (50 mm) is considered to be 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.

STEP 3—SELECTING OR DEVELOPING EXPECTANCY PERFORMANCE CURVES

Expectancy performance curves are used as a means to predict the time when a pavement’s

condition will reach a construction trigger threshold. While measuring the pavement condition 

can be used to respond to current conditions, long-term planning of future needs and 

optimization requires predicting future pavement changes. 
18

A key consideration in developing 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 that 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 state-of-the-practice is to base performance expectancy 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 permit

the application of mechanistic-based performance models. When empirical data are not available 

to formulate proper statistical models of future performance, expected performance curves 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 

to create expectancy curves, the curves should be updated over time with field measurements to 

improve their accuracy and applicability. Potential expectancy curve options include 

models based on design equations, empirical models, and agency time-based rules.

Models Based on Design Equations

Expectancy performance curves used for pavement design can be different than those used for 

pavement management. Nonetheless, they can be used for pavement management. Examples of 

models based on design equations include the following:

• Models based on the 1986 AASHTO Guide for Design of Pavement Structures and 

subsequent updates have been used by some agencies (see figure 5).(4) Pavement 

condition is expressed in terms of PSR or PSI, and resurfacing or reconstruction is 

indicated by the PSR level. When the predicted pavement PSR in an analysis cycle drops 

below a minimum tolerable condition based on highway functional classification, then 

resurfacing is indicated. Reconstruction is triggered if the PSR drops below the lower 

rehabilitation threshold if the section was not previously selected for resurfacing. While 

these models are still being used by some agencies, because PSR is used to measure 

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

required since it is most sensitive to pavement roughness. 

• FHWA developed a set of expectancy performance curves based on the AASHTO 

Mechanistic-Empirical Pavement Design Guide (MEPDG) for use in the pavement health 

track (PHT) analysis tool.(5) Simplified models based on use of the default level 3 

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

pavement condition measures adjusted for current and past observed levels. They include 

pavement distress and roughness prediction models. Figure 6 illustrates a hypothetical 

distress-based pavement design approach consisting of pavement roughness and 

two distress types. The use of specific types of pavement distresses as construction 

triggers at the network level allows greater flexibility in assigning and costing future 

construction needs. This permits the application of automated rational decision tree 
19

logic to select appropriate treatments based on multiple aspects of pavement condition. 

Moreover, models such as these can be simplified to a level commensurate with intensity 

of data collection and sensitivity of business decisions. 

Figure 5. Graph. Illustrated service histories of trial pavement designs incorporating future 

overlays.(4)

Figure 6. Graph. Multiple distress-based pavement design where one of the distresses 

reaches a maximum threshold limit. 
20

Empirical Models

These expectancy performance curves are based on observations of events. The following list

summarizes empirical approaches to modeling pavement performance: 

• Survivor curves: The various approaches to development of survivor curves based on

life table, Kaplan-Meier, or failure time theory approaches 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 that can be 

used to estimate future construction needs not properly reflected by the variability in past 

definitions of pavement death. 

• Failure Cox Proportional Hazard model: This statistical model form is recommended 

as the basis for future development in empirical expectancy curves for pavement 

management applications. The approach is well suited for pavement performance 

management data. 

• Numerical regression models: These models are based on finding 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. 

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

Bayesian statistical concepts. A key component in Bayesian statistics is the comparison 

of weight given between prior and posterior observations.

• Neural network models: These models are inspired by the way biological nervous 

systems, such as the brain, process information. They 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 from function and 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. 

Agency Time-Based Rules

The simplest future expectancy performance curve is a time-based rule for future construction. 

This type of rule does not require investing in field measurement devices and custom computer 

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

optimizing 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 
21

rotation sequence is established. An appropriate correction strategy is selected and applied 

during the target construction year. Thus, the expectancy curve for the time until the next 

construction event is the time remaining until the next target construction year. 

STEP 4—IDENTIFYING COLLECTION OF INPUTS

Collecting 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 current pavement conditions should properly be used 

to determine the impact of the data element on future construction requirements based on the 

current 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 expectancy 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 some pavements with even longer lifetimes (i.e., 50 years). 

Pavement Roughness

The use of high-speed longitudinal pavement profile equipment has become the generally 

accepted industry standard for measuring pavement roughness. The measurement technique is 

based on using an inertial profiler, which 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 (RN), profilograph index, rolling straight edge simulation, bump/dip 

detection, fault heights on jointed PCC pavements, slab curvature on jointed PCC pavements, 

and heavy truck dynamic loading index. If pavement roughness measurements are performed 

using inertial longitudinal profiler devices, as shown in figure 7, both network- and project-level 

data requirements can be satisfied using this common set of data. 

Figure 7. Photo. Inertial road profilers used to measure IRI, RN, and other 

roughness indices. 
22

Pavement Distress

Distress ratings can be performed by human raters driving on 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 

require raters to drive each route and 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 reduce the risk to the raters and provide a historical archive of 

images for use in project-level investigations. Fully automated pavement distress ratings systems

use computer algorithms to interpret distresses obtained from field images and/or threedimensional measurements. It offers the potential to reduce the cost of acquiring distress 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. 

Deflection measurement devices (e.g., falling weight deflectometers (FWDs) as shown in 

figure 8) currently used in practice must stop at each measurement location, which requires 

traffic control. To overcome traffic control requirements, agencies are in various stages of 

developing and implementing modern moving deflection measurement devices. The rolling 

wheel deflectometer can travel at highway speeds and does not require traffic control. While the 

maximum deflection measured by this device is useful in locating weak locations for follow-up 

studies, it does not provide additional analytical capabilities offered by devices that measure the 

shape of the deflection basin. The traffic speed deflectometer (TSD) is another type of moving 

deflection device that has sensors that measure the shape of the deflection basin (see figure 9

and figure 10). 

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 

penetration radar (GPR) technology. Pavement deflection measurements are mostly performed at 

the project level to develop design specifications for the most appropriate construction treatment. 

However, justification for network-level pavement deflection measurements can be based on the 

additional refinement in the decisionmaking process resulting from the ability provided by these 

data to determine the cost of future construction activities. 
23

Figure 8. Photo. FWD used for structural evaluation.

Figure 9. Photo. TSD used for structural evaluation.

Figure 10. Photo. TSD internal pavement response measurement instrumentation. 

Traffic Loads

For pavement management purposes, the preferred practice is to use weigh-in-motion (WIM)

scales at non-enforcement locations to measure actual pavement loads as opposed to legally 

enforced loads. In order to accurately predict future pavement performance, engineers need to 
24

know actual heavy loads being applied to a pavement. Access to permitted overloads granted by 

other divisions within an SHA to the pavement management division could be important to 

determine the need for future construction intervention events. 

At the network level, 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. This can simplify measuring heavy trucks into loaded and unloaded classes based on the 

simplest of WIM technology as shown in figure 11. 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. Project-level measurement of truck axle loads is almost never performed for 

pavement engineering purposes.

Figure 11. Photo. WIM sensor used to record truck axle and gross vehicle weights.

Climate

Climate data are at best a second-order consideration in the planning of future construction 

needs. Climate 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 changes and 

accounting for 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). Figure 12 shows a mini seasonal monitoring program (SMP) weather 

station, which is an example of equipment used to collect climatic data. 

Figure 12. Photo. Mini site-specific SMP weather station used to record climatic data.
25

In addition to the data inputs themselves, it is also important to give serious consideration to the 

following data-related issues:

• 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 expectancy 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.

• Sampling intervals and frequency: Collecting pavement condition data on a partial 

sample of the network is typically used to reduce data collection costs. While sensor data 

such as pavement roughness and rutting are collected continuously, distress data are 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 semiautomated distress surveys where interpretations of distress are made from video images, 

only a portion of the video image may be interpreted on a sample basis. The condition 

ratings from these samples are then used to represent the condition of a larger pavement 

segment. Most agencies use monitoring frequencies of 1, 2, or 3 years between pavement 

condition measurements. For cracking, a 1-year frequency appears to be the best so that 

the first appearance of cracking can be detected in order to fit an appropriate model for 

cracking prediction. Monitoring of IRI using automated sensor readings could be on a 

1- or 2-year interval. A study by Baladi concluded that longer intervals for crack 

monitoring may cause an overestimation of agency costs for pavement repair at the 

network level, while longer intervals than suggested for IRI monitoring may result in 

an underestimation of costs.(6)

STEP 5—ESTABLISHING A STRATEGY SELECTION PROCESS

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 pavement 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.
26

The current challenge to highway agencies is to create a rational basis to move from a worst-first 

to a best-first (i.e., based on lowest LCC or other optimization technique) allocation of available 

agency repair resources. The application of corrective construction treatments at the proper time 

can extend the time until more costly treatments are required. These optimal treatments are 

applied before too much damage has accumulated and 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 type of decision is appropriate for pavements 

that have accumulated significant structural damage but are still smooth and provide adequate 

service otherwise. 

The remainder of this section provides additional guidance concerning the network versus 

project analysis strategy selection process.

Network Analysis

At the pavement network level, the objective is to characterize the current condition state of 

pavements included in the system that require the consideration of appropriate corrective 

treatments. Since network-level data tend to be more aggregated and less specific in comparison 

to project-level data, the expectancy 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. 

The recommended practice at the network level is to use LCC concepts to optimize the selection 

of construction projects in the next cycle and forecast future construction needs. LCC concepts 

consider multiple streams of future construction activities driven by predicting future changes in 

pavement states. Optimization is based on 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 using a scale—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, optimization of the objective function should focus on minimizing the area under the 

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 based on the 

sophistication of the system processes. It is not uncommon that the construction cost assumptions 

used in the network optimization planning processes do not match those developed from projectlevel design considerations. Continual improvements are being made to field data collection 

techniques, construction cost models, and new expectancy prediction curves. Modern automated 

field data collection systems are capable of providing greater amounts of data with specificity 
27

that approaches what used to be considered project-level data. In order to use this increase in 

data availability from network-level measurements, updates to the sophistication of the 

construction triggers, threshold limits, and expectancy curves are required.

Strategy selection analyses at the network level are primarily based on computer algorithms 

capable of taking into account 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 the 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, LCC analysis can be supplemented with cost engineering considerations 

specific to each project site. Cost engineering concepts require more intensive inputs on the 

resulting effects 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 based on a greater range of treatments options.

Project construction programming is the last chance to integrate other related infrastructure 

constructions 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.

STEP 6—PERFORMING PERIODIC ASSESSMENTS AND UPDATES

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 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 use of other management methods. 
28

Updates 

As new materials and construction methods are implemented, the models used for both 

planning and designing 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. 
29

CHAPTER 4. RSI IMPLEMENTATION EXAMPLES

This chapter further describes the implementation steps introduced in chapter 3 to reinforce 

the RSI concept and process. Specifically, three hypothetical examples addressing the six RSI 

implementation steps are provided for illustration purposes.

LCC analyses are used as part of step 5 in the three examples; it is assumed that they are 

performed on each set of viable construction events described in the respective examples and that 

the lowest LCC scenario from the possible combinations of constructions events is selected for 

the pavement segment in question. Because of their importance to RSI, LCC considerations are 

discussed in a separate section at the end of this chapter.

EXAMPLE 1—SHA USING PHT ANALYSIS TOOL

Background

The PHT analysis tool was developed by FHWA for the Highway Economic Requirements 

System and the National Pavement Cost model, but it is also used by other SHAs. It is an 

engineering software application used for determining and reporting the health of pavement 

networks. The pavement models used in this tool are based on concepts developed for the 

AASHTO MEPDG, but they have been simplified (both models and procedures) for purposes of 

the tool.(5) Both structural (distress) and functional (IRI) pavement performance models are 

included in the tool. The primary source data for the tool is the 2010+ version of the HPMS 

program data tables. Data items (state or material type defaults) not provided by the HPMS 

program but are required by the new pavement models are provided as default data tables 

developed mostly using Long-Term Pavement Performance data and MEPDG default data 

tables. Alternatively, an SHA can input data from its PMS.

In this example, the RSI concept is adapted by an SHA using the PHT analysis tool within the 

context of pavement network-level analyses, where the network is comprised of primary routes 

(i.e., State routes). To limit its scope, only composite hot mix asphalt (HMA) overlays on jointed 

plain concrete pavements (JPCPs) are addressed in this example.

RSI Implementation

1. Setting Construction Triggers

The PHT analysis tool makes use of simplified MEPDG pavement performance models. 

Accordingly, the construction triggers for HMA/JPCP pavements are reflection cracking and 

pavement smoothness (IRI).

2. Setting Threshold Limits

Because the RSI concept is adapted for pavement network-level analysis, threshold limits have 

been established for the following construction events:

• Do nothing.
30

• Corrective maintenance (crack filling, patching, etc.).

• Rehabilitation (mill 1-inch (25-mm) plus 4-inch (100-mm) HMA overlay).

• Reconstruction (removal and replacement down to the subgrade).

The resulting construction trigger-threshold limits matrix for reflection cracking or IRI is provide 

in table 1.

Table 1. Reflection cracking or IRI construction triggers-threshold limits.

Construction Event

Reflection Cracking 

(percent) IRI, inches/mi (m/km)

Do nothing No cracking < 90 (< 1.42)

Corrective maintenance > 0 to 15 percent 90 to 150 (1.42 to 2.37)

Rehabilitation 15 to 25 percent 150 to 250 (2.37 to 3.94)

Reconstruction > 25 percent > 250 (> 3.94)

3. Selecting or Developing Expectancy Performance Curves

Simplified MEPDG reflection transverse cracking and smoothness (IRI) models are used in the 

PHT analysis tool for HMA/JPCP pavements. Accordingly, these models are used in this 

demonstration of the RSI concept. The reflection cracking model is given as follows:

(5)

Figure 13. Equation. Reflection cracking model.

Where:

RCRK = Percent of cracks reflected (percent area of reflection cracking assumes a reflected 

crack width of 1 ft (0.305 m)).

AGE = Pavement age (years after asphalt overlay placement).

a = 3.5 + 0.75 × Heff.

b = -0.688 − 3.373 × Heff − 0.9154.

c = 1.0.

Heff = HHMA − 1 (for JPCP with good joint load transfer efficiency (i.e., faulting < 0.03 inches 

(0.76 mm)) or HHMA − 3 (for JPCP with poor joint load transfer efficiency (i.e., faulting 

≥ 0.03 inches (0.76 mm)).

HHMA = Asphalt layer thickness.

The values of the reflective cracking model parameter d are presented in table 2.

RCRK = 100

1+2.718a (c)+ b(AGE)(d)
31

Table 2. Reflective cracking model parameter d.

Effective Asphalt Overlay 

Thickness, inches (mm)

Delay Cracking by 

2 Years

Accelerate Cracking 

by 2 Years

< 4 (< 100 mm) 0.6 3.0

4 to 6 (100 to 150 mm) 0.7 1.7

> 6 (>150 mm) 0.8 1.4

The IRI prediction model is as follows:

Figure 14. Equation. IRI prediction model.

Where:

IRI = Predicted IRI (inches/mi).

INI_IRI = Initial IRI (inches/mi; use MEPDG default of 63.4 inches/mi (1.0 m/km)).

MRUT = Total rutting (inches).

CRACK = Alligator cracking (percent lane area).

TRANS_CK = Transverse cracking (ft/mile).

SF = Site factor (function of mean annual precipitation; amount of fine sand, silt, and clay size 

particles in subgrade; mean annual freezing index; subgrade soil plasticity index; and age of 

pavement after asphalt overlay placement).

4. Identifying Collection of Inputs

To support the expectancy performance curves detailed in step 3, the following input data are

required for each pavement segment in the network:

• Inventory data: Age of pavement after asphalt overlay placement. This information 

is obtained from the agency’s PMS database or extracted from the agency’s 

construction records.

• Construction data: Asphalt overlay thickness; amount of fine sand, silt, and clay size 

particles in subgrade; and subgrade soil plasticity index. This information is obtained 

from the agency’s PMS database or extracted from the agency’s construction records.

• Climatic data: Mean annual precipitation and mean annual freezing index. This 

information is obtained from the NCDC. 

• Pavement roughness: A high-speed inertial profiler with at least two laser height sensors 

with accompanying accelerometers located in the wheel path used to compute IRI in the 

wheel paths at or near the speed limit. These roughness measurements are performed on 

an annual basis over the entire network.

• Pavement distress data: Reflection cracking, alligator cracking, transverse cracking, and 

rutting. The cracking information is obtained using fully automated means with video 

imaging collected in the field and computer algorithms used to interpret distresses 

IRI = INI_IRI + 40.8 × MRUT + 0.575 × CRACK + 0.0014 × TRANS_CK + 0.00825 × SF
32

obtained from field images. Rutting information is gathered using laser technology 

mounted on the same vehicle used to gather the distress data. The technology is capable 

of taking more than 1,000 transverse elevation measurements across the width of the 

pavement. Both cracking and rutting measurements are performed on an annual basis 

over the entire network.

• Pavement structural response: JPCP joint load transfer efficiency. This information is

gathered using a FWD unit. Deflection testing to establish joint load transfer efficiency is

performed on a 2-year cycle over the entire network, with measurements performed at 

1-mi (1.61-km) intervals.

5. Establishing a Strategy Selection Process

For each pavement segment in the network, LCC analyses are performed to identify the strategy 

having the lowest LCC. 

For those segments having more than 25 percent reflection cracking and/or an IRI value 

greater than 250 inches/mi (3.94 m/km) (RSI = 0 years), the LCC analysis is limited to the 

reconstruction strategy. For all other segments, more than one construction treatment, hence RSI, 

are considered. For example, for a segment having 5 percent reflective cracking and an IRI value 

of 120 inches/mi (1.89 m/km), the following scenarios developed using the PHT analysis tool 

will be considered (each having a unique RSI in terms of years to construction event):

• Preventive maintenance: RSI = 0 years.

• Rehabilitation: RSI = 5 years (reflective cracking) and RSI = 7 years (smoothness), so 

RSI = 5 years (controls).

• Reconstruction: RSI = 12 years (reflective cracking) and RSI = 16 years (smoothness), 

so RSI = 12 years (controls).

A 30-year LCC analysis will be performed for each of the viable construction events described, 

and the lowest LCC scenario will be selected for the pavement segment in question.

Due to budgetary constraints, the agency will prioritize the LCC results for all the pavement 

segments in the network to select those segments that will receive some sort of treatment in a 

given year. The prioritization will be done based on annual average daily traffic (AADT) and 

percent trucks. Once the segments that will receive a construction treatment have been selected, 

the project-level design of the construction event and subsequent construction will be turned over 

to the districts for implementation.

6. Performing Periodic Assessments and Updates

Implementing the RSI concept is anticipated to be completed within 1 year. Once finished, 

the agency will perform an assessment of the resulting system at the end of 1 year and every 

other year afterwards. Updates to the system will be planned based on the results of the 

assessments. In addition, the agency will implement necessary updates to keep pace with 
33

technology (e.g., improved expectancy curves and possible additional inputs, improved 

pavement monitoring equipment and analysis software, etc.).

EXAMPLE 2—SHA USING THE 1972 AASHTO INTERIM GUIDE FOR DESIGN OF 

PAVEMENT STRUCTURES (7)

Background

A State transportation department is using the 1972 AASHTO Interim Guide for Design of 

Pavement Structures pavement equations for both design and management purposes.(7) As such, 

the pavement condition of roads in its network is being monitored and forecasted in terms of 

PSR. The agency plans on implementing the RSI concept at both the project and network levels. 

The agency’s network is comprised of roads having multiple functional classifications, but all of 

them are either new HMA pavement or existing HMA pavement overlaid with HMA. 

RSI Implementation

1. Setting Construction Triggers

Because the agency is using the 1972 AASHTO Interim Guide for Design of Pavement Structures

pavement equations for both design and management purposes, the construction trigger for all 

pavement segments in the network will be PSR.(7)

2. Setting Threshold Limits

The RSI concept is to be adapted for pavement network- and project-level analyses. 

Accordingly, threshold limits have been established for the following construction events:

Network level:

• Do nothing.

• Maintenance.

• Milling and thick HMA overlay.

• Full reconstruction.

Project level:

• Do nothing.

• Preventive maintenance.

• Corrective maintenance.

• Milling and thin HMA overlay.
34

• Milling and thick HMA overlay.

• Partial reconstruction (replace HMA surface layer).

• Full reconstruction.

The resulting construction triggers-threshold limits matrices are provide in table 3 and table 4 for 

the network and project levels, respectively.

Table 3. Network-level construction trigger-threshold limits.

Construction Event Functional Class PSR

Do nothing All > 3.8

Maintenance

Primary 3.0–3.8

Secondary 2.8–3.8

Tertiary 2.6–3.8

Rehabilitation

Primary 2.0–3.0

Secondary 1.8–2.8

Tertiary 1.6–2.6

Reconstruction

Primary < 2.0

Secondary < 1.8

Tertiary < 1.6

Table 4. Project-level construction trigger-threshold limits.

Construction Event Functional Class PSR

Do nothing All > 3.8

Preventive 

maintenance

Primary 3.2–3.8

Secondary 3.0–3.8

Tertiary 2.8–3.8

Corrective 

maintenance

Primary 2.8–3.2

Secondary 2.6–3.0

Tertiary 2.4–2.8

Mill/thin HMA 

overlay

Primary 2.4–2.8

Secondary 2.2–2.6

Tertiary 2.0–2.4

Mill/thick HMA 

overlay

Primary 2.0–2.4

Secondary 2.0–2.2

Partial reconstruction All 1.6–2.0

Full reconstruction All < 1.6

3. Selecting or Developing Expectancy Performance Curves

The 1972 AASHTO Interim Guide for Design of Pavement Structures flexible pavement design 

equation is used for both design and management purposes.(7) Accordingly, this equation, shown 

in figure 15, will be used in the implementation of the RSI concept. 
35

Figure 15. Equation. Flexible pavement design equation.

(7)

Where:

Wt18 = Equivalent 18-kip (40-kN) single-axle load application.

R = Regional factor.

Si = Soil support value.

The number equivalent 18-kip (40-kN) single-axle load applications, or Wt18, is computed as a 

function of the AADT, direction split, percent trucks, lane distribution, and traffic growth factor. 

Equivalency factors are used to relate different axle loads to 18-kip (40-kN) single-axle load 

applications. For pavement management purposes (i.e., for RSI), Wt18 is also expressed in terms 

of time (years), which are computed using the same inputs just referenced.

The structural number (SN) is defined as follows:

Figure 16. Equation. SN.

Where:

ai = Structural layer coefficient of the various layer that make up the pavement structure 

(a function of material type and properties).

di = Thickness of the various layers that make up the pavement structure.

The regional factor or R value varies between 0.2 and 5.0 depending on subsurface moisture and 

temperature conditions, while the soil support or Si value is established based on correlations 

with standard soil tests (e.g., California bearing ratio, R, triaxial strength, etc.).

To account for differences in predicted versus actual PSR values, the agency has developed a 

linear shift factor for the expectancy curve based on time/traffic (i.e., Wt18).

4. Identifying Collection of Inputs

To support the expectancy performance curve detailed in step 3, the following input data will be 

required for each pavement segment in the network:

• Inventory data: Age of pavement since construction or since asphalt overlay placement. 

This information is needed for the Wt18/RSI computations and will be obtained from the 

agency’s PMS database or extracted from the agency’s construction records.

• Construction data: Pavement material layer types, thicknesses, and properties. 

This information is needed to determine the SN and Si values and will be obtained 

from the agency’s PMS database or extracted from the agency’s construction records. 

Alternatively, this information may be gathered using non-destructive, destructive, and/or 

laboratory testing.

PSR = f(equivalent 18-kip [40-kN] single-axle load applications or Wt18, structural number or 

SN of the pavement, regional factor or R, and soil support or Si value)

SN = Σai × di
36

• Climatic data: General information on the subsurface moisture and temperature 

conditions. This information is needed to establish the R value and will be established on 

the basis of the agency’s experience of local conditions. 

• Traffic data: AADT, direction split, percent trucks, lane distribution, and traffic growth 

factor. This information will be obtained from existing local or regional traffic counts. 

Alternatively, if the data are to be used for project-level analysis, actual traffic counts will 

be carried out.

• Pavement roughness: A high-speed inertial profiler with three laser mounted sensors on 

the front bumper will be used to measure PSR using existing pavement roughness 

correlation. These roughness measurements will be performed on a 2-year cycle over the 

entire network.

• Pavement structural response: Pavement material layer types, thicknesses, and 

properties. If this information is not readily available, consideration will be given to using 

a FWD unit in conjunction with a GPR unit, especially if the data are to be used for 

project-level analysis. The resulting deflection and layer thickness data will be used to 

compute the effective SN of the pavement, either through the backcalculation of 

individual layer moduli or through direct computation of SN. 

5. Establishing a Strategy Selection Process

For network-level analyses, the same strategy selection process as described in example 1 will 

be used (i.e., LCC analyses are performed for each pavement segment to identify the strategy 

having the lowest LCC). For example, for primary segments having a PSR less than 2.0 

(RSI = 0 years), the LCC analysis is limited to the reconstruction strategy. The same applies for 

secondary segments having a PSI less than 1.8 and tertiary segments having a PSR less than 1.6. 

For all other segments, more than one construction treatment, hence RSI, are considered. For 

example, for a primary segment having a PSR of 3.0, the following scenarios developed using 

the 1972 AASHTO Interim Guide for Design of Pavement Structures pavement design equation 

are considered (each having a unique RSI, in terms of years to construction event):(7)

• Maintenance: RSI = 0 years.

• Rehabilitation: RSI = 6 years.

• Reconstruction: RSI = 15 years.

A 30-year LCC analysis is performed for each viable construction event, and the lowest LCC 

scenario is selected for the pavement segment in question.

Like the first example, due to budgetary constraints, the agency prioritizes the LCC results for all 

pavement segments in the network to select the segments that will receive some sort of treatment 

in a given year. The prioritization is done based on functional class, AADT, and percent trucks.
37

LCC analyses are also performed at the project level using the expanded (project-level) 

construction events trigger-level matrix. According to this matrix, pavement segments having a 

PSI less than 1.6 (RSI = 0 years) will undergo full reconstruction, while pavement segments 

having a PSI of 1.8 may undergo partial reconstruction (RSI = 0 years) or full reconstruction 

(RSI > 0 years). Similarly, for a primary segment with a PSI of 3.6, the following possible 

construction treatments (hence RSIs) will be considered:

• Preventive maintenance: RSI = 0 years.

• Corrective maintenance: RSI = 3 years.

• Mill/thin HMA overlay: RSI = 9 years.

• Mill/thick HMA overlay: RSI = 12 years.

• Partial reconstruction: RSI = 17 years.

• Full reconstruction: RSI = 25 years.

Unlike the network-level analysis, however, the chosen construction event (and hence RSI) at the 

project level is not selected based solely on LCC. Rather, the LCC analysis is supplemented with

cost engineering considerations specific to each project segment (i.e., cost/benefit analysis of 

alternative construction treatments using tool developed for the agency). Moreover, each 

construction event (e.g., overlay thickness) is designed based on project-specific data 

(i.e., not a standard treatment for each construction event but tailored to the project).

6. Performing Periodic Assessments and Updates

Implementation of the RSI concept is anticipated to be completed within 18 months. The first 

12 months will be spent setting up and fully testing the system, while the remaining 6 months 

will be spent rolling out the RSI concept within the agency, including training agency personnel. 

Once implemented, the agency plans on assessing the resulting system on an annual basis. 

Changes and updates to the system will be implemented based on the results of the assessments 

as well as on new technology. In addition, because the agency plans on migrating to the 

AASHTO MEPDG in 5 years, a targeted effort will be dedicated to updating the RSI system 

within the context of the MEPDG performance models, including the assessment and/or 

revisions to construction triggers, threshold limits, expectancy curves, collection of inputs, 

strategy selection process, and future assessments and updates.

Numerical Example:

Consider a pavement network consisting of three primary roadways, two secondary roadways,

and a tertiary roadway. For this small network, it is assumed that the regional factor would be 

equal throughout, and the typical average annual value of 1 is used. Soil type throughout the 

network ranges from silty clay to sandy gravel, and the age of the pavements and traffic loadings 

account for some of the variation and rate of change in PSR values.
38

Table 5 contains the current PSR value and the predicted PSR value after 3 and 10 years. The 

PSR values degrade at different rates due to the differences in traffic loading, pavement age, and 

structural properties. Based on the construction trigger-threshold limits matrix for the network 

level, the years to construction event (RSI) is given for each type of construction treatment based 

on the 1972 AASHTO Interim Guide for Design of Pavement Structures pavement design 

equation for the given PSR values (see figure 15).

(7)

Table 5. RSI based on PSR values and construction treatment.

Pavement 

ID

Functional 

Class

PSR 

Current

PSR

3 Years

PSR 

10 Years

Construction 

Treatment

Current

RSI 

(years)

3-Year 

RSI 

(years)

10-Year 

RSI 

(years)

1 Primary 3.6 3.2 2.3

Maintenance 0 0 N/A

Rehabilitation 6 3 N/A

Reconstruction 15 11 3

1 Primary 3.0 2.7 2.1

Maintenance 0 N/A N/A

Rehabilitation 4 2 < 1

Reconstruction 9 7 1

2 Primary 2.8 2.3 1.3

Maintenance N/A N/A N/A

Rehabilitation 3 2 N/A

Reconstruction 8 3 0

3 Secondary 3.4 3.1 2.5

Maintenance 0 0 N/A

Rehabilitation 6 4 2

Reconstruction 15 12 6

4 Secondary 2.9 2.7 2.2

Maintenance 1 N/A N/A

Rehabilitation 4 3 1

Reconstruction 10 8 4

5 Tertiary 3.2 3.0 2.6

Maintenance 3 2 0

Rehabilitation 6 4 2

Reconstruction 15 13 7

N/A = Not applicable.

Some pavement sections have PSR values below the threshold limit for the respective 

construction trigger; therefore, that construction type is not applicable to that pavement section,

and the next construction type must be considered. 

The RSI values are based on the threshold limits for each construction trigger and not the age of 

the pavement section. This explains why the difference between the current RSI and the RSI 

after 10 years (or 3 years) is not always 10 years (or 3 years).

EXAMPLE 3—SHA USING MULTIPLE PERFORMANCE INDICATORS

Background

An SHA manages a large network made up of primary State routes only that are comprised 

entirely of JPCP of varying ages ranging from less than 1 year to 15 years. After several months 

of deliberations and planning, the agency has recently embarked in the migration of their 

pavement design and management procedures from the 1986 AASHTO Guide for Design of 
39

Pavement Structures.

(4) As part of this migration, the agency has decided to include pavement 

structural capacity and surface friction as additional pavement performance indicators. 

Moreover, the agency has decided to move forward with the adoption of the RSI concept but is 

making no distinction between network- and project-level analyses based on the intensive data 

collection activities they currently have in place.

RSI Implementation

1. Setting Construction Triggers

The agency has embarked in the implementation of the RSI concept using multiple performance 

indicators. Those indicators include the following:

• AASHTO MEPDG—pavement surface distress.

(5)

o Transverse cracking.

o Transverse joint faulting.

• AASHTO MEPDG—pavement smoothness.

(5)

o IRI.

• Pavement structural capacity.

o Ratio of current to initial (at time of construction or overlay) effective PCC thickness 

(based on standard modulus of 4,000,000 psi (27,560 MPa)).

• Pavement surface friction.

o Friction number (FN).

2. Setting Threshold Limits

Because the RSI concept is to be adapted for both network- and project-level analyses, without 

distinction in terms of construction events, threshold limits have been established for the 

following construction events: 

• Do nothing.

• Preventive maintenance (joint sealing, etc.).

• Corrective maintenance (crack filling, patching, etc.).

• Diamond grinding of JPCP surface.

• Grooving of JPCP surface.
40

• CPR.

• Ultra-thin PCC overlay.

• PCC overlay of existing PCC material, where thickness of new material is determined in 

accordance with the AASHTO MEPDG procedures.(5)

• Removal and replacement of PCC surface layer.

• Complete removal and replacement of the pavement structure down to the subgrade, 

where the replacement pavement is determined in accordance with the AASHTO 

MEPDG procedures and can be either a HMA or PCC structure.(5)

The resulting construction trigger-threshold limits matrix is provide in table 6.

Table 6. Construction trigger-threshold limits matrix.

Construction 

Event

Transverse 

Cracking 

(Percent

Area)

Transverse 

Joint 

Faulting, 

inches (mm)

IRI, 

inches/mi 

(m/km)

Effective 

PCC 

Thickness 

Ratio FN

Do nothing 0 < 0.05 

(< 1.27)

< 90 (< 1.42) > 0.95 > 60

Preventive 

maintenance

> 0–1 N/A N/A N/A N/A

Corrective 

maintenance 

1–3 N/A N/A N/A N/A

Diamond 

grinding

N/A 0.05–0.10 

(1.27–2.54)

90–120 

(1.42–1.89)

N/A 40–60

Grooving N/A N/A N/A N/A < 40

CPR 3–5 N/A N/A 0.95–0.85 N/A

Ultra-thin PCC 

overlay

5–10 0.10–0.15 

(2.54–3.81)

120–175 

(1.89–2.76)

0.85–0.75 N/A

PCC overlay 10–15 0.15–0.25 

(3.81–6.35)

175–250 

(2.76–3.94)

0.75–0.65 N/A

Removal and

replacement of 

PCC

15–25 > 0.25 

(< 6.35)

> 250 

(> 3.94)

0.65–0.50 N/A

Removal and

replacement of 

pavement

> 25 N/A N/A < 0.50 N/A

N/A = Not applicable.

3. Selecting or Developing Expectancy Performance Curves

Since the SHA is migrating from the 1986 AASHTO Guide for Design of Pavement Structures to 

the AASHTO MEPDG, the pavement performance models contained in the latter guide have 
41

been selected for implementing the RSI concept without deviations.(4,5) These models are 

applicable to the following pavement performance indicators: 

• Transverse cracking.

• Transverse joint faulting.

• Pavement smoothness (IRI).

For the pavement structural capacity performance indicator, the agency will use the ratio of 

current to initial (at time of construction or overlay) effective PCC thickness, where thickness is 

determined in terms of an equivalent standard modulus of 4,000,000 psi (27,560 MPa) using 

backcalculated layer moduli data and equivalencies based on the stiffness equation. The agency 

has chosen to proceed with this ratio as a result of the extensive FWD deflection testing program 

it has had carried out over the past 15 years. Specifically, effective PCC thickness ratio

prediction models have been developed for the various regions in the State from the massive 

amount of deflection data collected and analyzed over the years. 

To account for the seasonal variations in the deflection data, the agency has also developed 

temperature and moisture correction factors to adjust the effective PCC thickness ratio to a 

standard temperature and moisture state. In addition, the agency has developed shift factors for 

the prediction models to account for differences between predicted and measured ratios.

In terms of the pavement surface friction performance indicator, the SHA has been working 

closely with a neighboring SHA to develop a FN performance model based on data they have 

gathered over the past decade on JPCP pavements. Jointly, these two agencies have determined 

that the initial FN for their pavements is 65 on average and that there is a constant rate of 

deterioration (decrease in FN) equal to -0.80 FN per year. For example, a new pavement that has 

been in service for 10 years is expected to have a FN = 57. Similarly, a pavement that presently 

has a FN = 42 is expected to fall below FN = 40 in less than 3 years. As was the case with the 

effective PCC thickness ratio, the agency has developed shift factors for the FN prediction model 

to account for differences between predicted and measured FN values.

4. Identifying Collection of Inputs

To support the expectancy performance curves detailed in step 3, the following input data will be 

required for each pavement segment in the network: 

• Inventory (e.g., age of pavements since construction), construction (e.g., PCC thickness 

and properties), climate (e.g., temperature data), and traffic (e.g., load spectra and 

volumes) data. Some of this information will be obtained from the agency’s PMS 

database or extracted from the agency’s construction records. However, because the 

agency is migrating to the AASHTO MEPDG and intends on using level 2 data, a 

number of data collection efforts are underway to obtain the necessary information 

(e.g., collection of load spectra data at various locations throughout the network using 

WIM technology, materials laboratory testing to characterize the most commonly 

used pavement materials and subgrades, extraction of appropriate climatic data from 
42

the NCDC, use of GPR for determining pavement layer thicknesses throughout the 

network, etc.)

• Pavement performance data. In support of the various expectancy curves that will be 

implemented as part of the migration to the AASHTO MEPDG as well as the RSI 

concept, the agency plans on purchasing two fully integrated and automated devices to 

collect and interpret pavement distress data (transverse cracking and transverse joint 

faulting) and smoothness (IRI) data at highway speeds using various laser-mounted 

sensors and high-resolution video cameras. In support of the effective PCC thickness 

ratio model, the agency will be performing FWD deflection testing and analysis at 0.5-mi

(0.8-km) intervals through the network. Finally, in support of the FN model, the agency 

will perform skid measurements at 0.5-mi (0.8-km) intervals through the network using a 

lock-wheel skid trailer at speeds of 40 mi/h (64 km/h). All of these measurements will be 

performed on a 2-year cycle over the entire network. In addition, more intensive (in terms 

of spacing between measurements) FWD and friction measurements may be performed at 

the project level on an as needed basis.

5. Establishing a Strategy Selection Process

The strategy selection process that will be used is the same for both network- and project-level 

analyses; an LCC analysis-based optimization technique developed by the State’s main 

university will be used to identify the optimal construction treatments at the appropriate times. 

The only distinction will be the amount of data that will be used to support project-level analyses 

(i.e., FWD deflection and skid testing at shorter intervals (as compared to those described in 

step 4) within the segment units.

To illustrate the strategy selection process, the following example is provided. A 5-year-old 

pavement segment has been determined (through measurements and/or predictions using 

expectancy curves) to be in the following condition: transverse cracking = 2 percent, transverse 

joint faulting = 0.12 inches (3 mm), IRI = 80 inches/mi (1.26 m/km), effective PCC thickness 

ratio = 0.90, and FN = 50.

Based on the performance condition information and using the selected and developed 

expectancy curves, the RSIs for the segment in question and the associated construction

treatments are summarized in table 7.

Based on the information in the table, it should be apparent that corrective maintenance, ultrathin PCC overlay, and diamond grinding are required (i.e., RSI = 0 years) to address transverse 

cracking, transverse joint faulting, and FN, respectively. Clearly, the ultra-thin PCC overlay 

trumps the other two construction treatments, but this does not imply that the agency will 

proceed with this construction treatment or one of the other two, as it may be more cost effective 

to place a PCC overlay in 6 years.
43

Table 7. RSI based on construction treatments.

Construction 

Event

Transverse 

Cracking

Transverse 

Joint 

Faulting IRI

Effective 

PCC 

Thickness 

Ratio FN

Do nothing N/A N/A 0 years 0 years N/A

Preventive 

maintenance

N/A N/A N/A N/A N/A

Corrective 

maintenance 

0 years N/A N/A N/A N/A

Diamond grinding N/A N/A 5 years N/A 0 years

Grooving N/A N/A N/A N/A 13 years

CPR 4 years N/A N/A 7 years N/A

Ultra-thin PCC 

overlay

12 years 0 years 14 years 13 years N/A

PCC overlay 18 years 6 years 21 years 20 years N/A

Removal and

replacement of PCC

24 years 12 years 28 years 26 years N/A

Removal and

replacement of 

pavement

32 years 19 years N/A 30 years N/A

N/A = Not applicable.

Based on this example, it is apparent that selecting the timing and the type of construction 

treatment to be applied to the segment in question or any other segment in the network is not an 

easy task. In turn, this is the reason the agency has decided to use the LCC analysis-based 

optimization technique to facilitate the strategy selection process. The technique uses the 

following steps for each segment in the network:

1. For each pavement performance indicator in question, the technique is used to determine the 

RSI (time in years) of the segment for each applicable construction event. For the transverse 

cracking indicator provided in the example, RSI results are as follows:

• Corrective maintenance: RSI = 0 years.

• CPR: RSI = 4 years.

• Ultra-thin PCC overlay: RSI = 12 years.

• PCC overlay: RSI = 18 years.

• PCC removal and replacement: RSI = 24 years.

• Removal and replacement of pavement structure: RSI = 32 years.

2. Based on the information generated in step 1, a logical list of RSI and construction treatments

is generated. It makes little sense, for example, to apply corrective maintenance and an 
44

overlay during the same year. Similarly, it makes little sense to place an overlay and then 

reconstruct the pavement 2 years later. A logical list of construction treatments and RSI 

values for the example provided is as follows: 

• Corrective maintenance and diamond grinding: RSI = 0 years.

• CPR and diamond grinding: RSI = 4 years.

• PCC overlay: RSI = 6 years.

• Removal and replacement of PCC: RSI = 12 years.

• Removal and replacement of pavement: RSI = 19 years.

3. A 25-year LCC analysis is then performed for each construction event included in the logical 

list from step 2. The sequence of construction treatments with the lowest LCC value is 

selected for the segment in question (e.g., PCC overlay in year 6).

Once the three steps are completed, the total network budget requirements for the next 5 years

are considered. If the requirements are greater than the available funds, additional analyses are 

performed by considering other options beyond the lowest LCC. More specifically, the same 

three steps are completed by looking at the next lowest LCC value for each segment having 

lower costs within the first 5 years until the optimum network solution is identified.

6. Performing Periodic Assessments and Updates

Given the scope of work, the effort is anticipated to be completed in 2 years. Once finished, 

the agency will perform assessments every 6 months during the first 2 years and annual 

assessments afterwards. Changes and updates to the system will be implemented based on 

the results of these assessments. In addition, the agency will perform updates to incorporate 

improved AASHTO MEPDG, structural capacity, and/or FN performance models. Moreover, in 

their efforts to fully automate all of their pavement performance data collection, the agency plans 

on replacing its current FWD fleet in 5 years with a moving pavement deflection testing device 

capable of measuring deflection basins. As such, an updated version of the effective PCC 

thickness ratio model will be required in support of the RSI processes. 

LCC CONSIDERATIONS

LCC analyses were purposely used as part of the strategy selection process (step 5) in all 

three examples due to their importance to the decisionmaking process. A summary of LCC 

analysis, as presented by FHWA, is as follows:

(8)

• LCC analysis is an engineering economic analysis tool useful in comparing the relative 

economic merits of competing construction or rehabilitation design alternatives for a 

single project. By considering all of the relevant costs (agency and user) incurred during 

the projected service life of an asset, this analytical process helps transportation officials 

identify the lowest cost option. Additionally, LCC analysis introduces a structured 

methodology that quantifies the effects of agency activities on transportation users and 
45

provides a means to balance those effects with the construction, rehabilitation, and 

preservation needs of the system itself.

• LCC analysis can be used in making certain transportation investment decisions. 

Specifically, when a project has been selected for implementation, LCC analysis will 

assist in determining the lowest cost way to accomplish the performance objectives of the 

project. LCC analysis cannot, however, be used to compare the economic merits of 

projects that provide different levels of benefits for highway users, such as comparing a 

road-widening project with a rehabilitation of existing roadway lanes. LCC analysis 

accounts only for cost differences between project alternatives and is applicable only to 

decisions where benefits are equal for all alternatives being considered.

• The LCC analysis process begins with the development of alternatives to accomplish 

the structural and performance objectives for a project. The analyst then defines the 

schedule of initial and future activities involved in implementing each of the project 

design alternatives. Next, the costs of these activities are estimated. Best practice LCC 

analysis calls for including not only direct agency expenditures (e.g., construction or 

maintenance activities) but also costs to the project’s users that result from agency work 

zone operations.

• The predicted schedule of activities and their associated agency and user costs form the 

projected LCC stream for each design alternative. Using an economic technique known 

as discounting, these costs are converted into present dollars and then summed for each 

alternative. The analyst can then determine which alternative is the most cost effective.

In the three examples presented in this chapter, it was assumed that LCC analyses were 

performed for each of the viable construction events described in the respective examples and 

that the lowest LCC scenario from the possible combinations of constructions events was 

selected for the pavement segment in question. In the first example, the combinations of 

construction events considered over the selected 30-year analysis period included corrective 

maintenance, rehabilitation, and reconstruction. A partial set of possible combinations of 

construction events and schedule of activities for the first example is provided in figure 17.

It is important to note that in all three examples, the RSI for reconstruction is not independent of 

preservation and/or rehabilitation considerations nor is the RSI for rehabilitation independent of 

preservation considerations in the life-cycle assumptions used as the basis for the determination. 

The streams of LCCs consist of a future stream of activities, which include preservation, 

rehabilitation, and reconstruction treatments. To a large extent, the level of dependency is 

dictated by the pavement performance expectancy curves selected as part of the RSI process.
46

Figure 17. Flowchart. Partial set of construction event combinations and schedule of 

activities for example 1.

It is also important to note that the lowest LCC option may not necessarily be implemented when 

other considerations such as risk, available budgets, and political and environmental concerns are 

taken into account. LCC analysis provides critical information to the overall decisionmaking 

process but does not necessarily provide the final answer. This is the case in the third example, 

where options other than the lowest LCC may need to be considered and selected in order to 

determine a total network budget for the next 5 years that is within the available funds.

Pavement Network Segment X 

with Current Condition of Y

Rehabilitation,

Year 7

Reconstruction,

Year 15

Maintenance, 

Year 3

Reconstruction,

Year 17

Maintenance, 

Year 25

Rehabilitation,

Year 9

Rehabilitation, 

Year 27

Maintenance, 

Year 28
47

CHAPTER 5. COMMUNICATING RSI FRAMEWORK RESULTS

While the RSI concept provides an alternative to the long-standing and confusing RSL 

terminology, the outcomes from the RSI process can be used, presented, and communicated in 

the same fashion SHAs have been doing for years using RSL. The RSI concept does not provide 

an alternative to assessing the health of the network or making decisions about how to spend 

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 LCC and/or other prioritization 

approaches based on streams of future construction events).

An added benefit of adapting the RSI terminology is that the methodology provides a readily 

available way to communicate impacts of alternate budget scenarios. If the streams of optimum 

future construction event sequences are stored in a database format that allows the use of 

automated tools to group similar construction events into categories for planning purposes, then 

the following information can be easily generated:

• Cost of optimum mix of pavement preservation, rehabilitation, and reconstruction to meet 

total needs during the current planning cycle. 

• Current backlog carryover of unmet needs from the last planning cycle, including

increased construction costs due to deferred maintenance and rehabilitation. 

• Optimum mix of construction treatments at alternative budget levels, which are less than 

the total needs.

• Impacts of less-than-optimal budget levels on future backlogs of deferred 

construction events.

• Needed capital budgets for future planning cycles.

• Rational basis to include both pavement condition and other non-pavement condition 

factors in the future construction planning process without using confusing terms such as 

service versus structural needs.

At the technical level (i.e., pavement engineers, managers, and technicians working at State 

transportation departments and local agencies as well as highway contractors), the new RSI 

terminology should not be difficult to understand nor should the RSI implementation steps. The 

biggest challenge will be for engineers to substitute a term they have been using often for most 

of their careers (RSL) with a new one (RSI). While a challenge, it is simply a matter of time 

before the new terminology replaces the old one, especially given the simplicity and logic of the 

new terminology.

At the upper management level (i.e., chief engineers of State and local agencies and Federal, 

State, and local government decisionmakers), two possible scenarios exist. One scenario includes

managers who are not aware of the RSL concept. As a result, the new RSI concept is of little 

consequence to them; they are mostly interested in budgets and other political considerations. 
48

The other scenario includes decisionmakers who are aware of and have been using the RSL 

concept. Regardless of the scenario, the fact is that people must determine which projects will 

be funded and when.

Perhaps the most important message to convey to decisionmakers is that pavements are 

repairable systems—the life of the system is not defined by failure of a few components. As 

such, the use of remaining life is not only confusing but also inappropriate. Material developed 

to convey this message needs to be short, to the point, and focused. The objective is to provide 

decisionmakers with easily understood information that reduces potential for misinterpretation, 

provides a rational basis to assess current status, and promotes better decisions. 

In summary, adopting the RSI concept should not significantly affect the day-to-day operations 

once the shift in terminology is accomplished. Most or all of the RSI implementation steps 

should already be in place at most SHAs. 

Other than the change in terminology, reporting and communicating the outcomes from 

these steps should not be affected by the RSI concept (e.g., practitioners will need to know 

current and future conditions and perform LCC analyses to identify lowest cost strategies, while 

decisionmakers will need to know what budget is required to maintain the network at or above a 

minimum level).
49

APPENDIX. GENERIC AGENCY ISSUES

This appendix addresses issues associated with implementing the RSI concept within an agency. 

While not part of the actual RSI process itself, the issues covered are critical to the success of an 

agency’s RSI implementation effort. These issues address the establishment of the agency’s RSI 

protocol, the identification of an RSI coordinator, and the dissemination of the RSI concept 

within the agency. These issues only need to be addressed once, with periodic monitoring and 

revising to ensure they are still appropriate; however, they are vital to the success of an agency’s 

RSI program.

ESTABLISHING AN RSI PROTOCOL

One issue is establishing an agency protocol for implementing the RSI concept. Adoption and 

acceptance of this protocol will help formalize the RSI implementation effort. This protocol 

should be communicated to individuals in the agency, and they should be encouraged to follow 

it. The protocol should include the following elements:

• Reasons for having the protocol.

• Agency’s implementation plan.

o Approach to implementation steps.

o Implementation schedule.

o Allocation of resources.

• Roles and responsibilities.

• Training of agency personnel.

IDENTIFYING AND APPOINTING AN RSI COORDINATOR

To ensure the successful implementation of the RSI concept within an agency, it is important 

to select an individual to lead the RSI effort and define his/her responsibilities. This person, 

designated the agency’s RSI coordinator, is expected to be involved in all facets of the agency’s 

implementation effort (and possibly in establishing the protocol for the agency). This activity is 

not anticipated to require a full-time position.

Key responsibilities of the coordinator, who should have an appropriate level of decisionmaking 

authority, include, but are not limited to, the following:

• Acting as the central point of contact for all RSI issues within the agency.

• Implementing the RSI process within the agency.

• Training agency personnel and disseminating RSI information.
50

• Periodically performing RSI assessments and updates.

To properly perform these responsibilities, the agency’s coordinator is expected to have the 

following qualifications:

• Possess sufficient stature within the agency to make decisions on implementing the RSI 

concept and to delegate activities and responsibilities to others.

• Have sound understanding, background, and experience in pavement design and 

performance-related issues.

• Possess good project management skills, including sufficient leadership and 

communications skills, to organize the various activities associated with forensic 

investigations within the agency.

The agency’s coordinator should also be given the responsibility of recommending changes to 

the RSI concept and process based on the findings from the implementation effort and/or 

assessments and updates and be provided with the necessary technical and administrative support 

to properly and efficiently address RSI issues within the agency.

DISSEMINATION OF RSI CONCEPT WITHIN AGENCY

The following list highlights dissemination options that an agency may consider in order to 

facilitate implementation of the RSI concept and improve the likelihood of success:

• RSI Web site: Most agencies maintain a Web site with links to various types of 

information relevant to the agency, including information about the pavements program. 

The addition of a RSI link within the agency’s pavement information area is a simple 

means for making the information available to a large group of potential users both 

within and outside the agency. 

• Paper reports, flyers, brochures, etc.: In addition to the Web site, the agency may 

distribute relevant RSI information in paper format to individuals/offices most affected 

by the implementation of the RSI concept. 

• Workshops, Webinars, and conferences: Presentations by the agency’s RSI coordinator 

or others at agency in-house pavement-related workshops, Webinars, and conferences are 

also an effective method of disseminating the RSI concept and its implementation 

process. Periodic Webinars are an appropriate and cost effective means of disseminating 

the RSI concept and process to key individuals within agencies.

It is also recommended that agencies develop a standard of practice regarding input assumptions 

used by agencies and contractors performing LCC analysis for the agency. While the amount 

of information available on how long future construction treatments will last varies drastically 

between SHAs, codifying a set of rules to be used for LCC analysis is the first step in the quality 

management improvement process. Once implemented, as more information is gained over time, 

the rules will evolve to match the actual agency experience.
51

REFERENCES

1. Elkins, G.E., Thompson, T.M., Groeger, J.L., Visintine, B., and Rada, G.R. (2013). 

Reformulated Pavement Remaining Service Life Framework, Report No. FHWA-HRT-13-

038, Federal Highway Administration, Washington, DC.

2. Federal Highway Administration. AASHO Road Test, U.S. Department of Transportation, 

Washington, DC. Obtained from: http://www.fhwa.dot.gov/infrastructure/50aasho.cfm.

3. AASHTO. (1993). AASHTO Guide for Design of Pavement Structures, American 

Association of State Highway and Transportation Officials, Washington, DC.

4. AASHTO. (1986). AASHTO Guide for Design of Pavement Structures, American 

Association of State Highway and Transportation Officials, Washington, DC.

5. AASHTO (2008). Mechanistic-Empirical Pavement Design Guide, A Manual of Practice, 

Interim Edition, American Association of State Highway and Transportation Officials,

Washington, DC.

6. Haider, S.W., Chatti, K., Baladi, G.Y., and Sivaneswaran, N. (2011). “Impact of Pavement 

Monitoring Frequency on Pavement Management System Decisions,” Transportation 

Research Record 2225, Transportation Research Board, Washington, DC.

7. AASHTO. (1972). AASHTO Interim Guide for Design of Pavement Structures, American 

Association of State Highway and Transportation Officials, Washington, DC.

8. Federal Highway Administration. (2002). Life Cycle Cost Analysis Primer, U.S. Department 

of Transportation, Washington, DC.

53

BIBLIOGRAPHY

1. Ahammed, M.A. and Tighe, S.L. (2009). “Early Life, Long Term and Seasonal Variations of 

Flexible and Rigid Pavement Skid Resistance,” Transportation Research Record 2094, 

Transportation Research Board, Washington, DC.

2. Alam, M. (2007). Pavement Remaining Life Analysis for National Policy Analysis, National 

Pavement Management Conference, Norfolk, VA.

3. Newcomb, D.E., Willis, R., and Timm, D.H. (2002). Perpetual Asphalt Pavements: A 

Synthesis, Report APA 101, Asphalt Pavement Alliance, Lanham, MD.

4. Baladi, G. (1991). Analysis of Pavement Distress Data, Pavement Distress Indices, and 

Remaining Life: An Advanced Course in Pavement Management Systems, Boston, MA.

5. Building Research Board. (1991). Pay Now or Pay Later, National Research Council,

Washington, DC.

6. Carey, W. and Irick, P. (1960). “The Pavement Serviceability-Performance Concept,”

Highway Research Board, 40–58, Washington, DC.

7. California Department of Transportation. (2007). Life-Cycle Cost Analysis Procedures 

Manual, California Department of Transportation, Sacramento, CA.

8. CTC & Associates, LLC. (2004). Pavement Service Life—Rev. 2, Transportation Synthesis 

Report, Wisconsin Department of Transportation, Madison, WI.

9. Emery, J.J., Lee, M.A., and Kamel, N. (1982). Skid Resistance Predictive Models, ASTM 

International, Conshohocken, PA.

10. Farrell, F.B. and Paterick, H.R. (1948). “Life Characteristics of Highway Surfaces,” Highway 

Research Board Proceedings, 28, Washington, DC. Federal Highway Administration. 

(2004). Life-Cycle Cost Analysis: Realcost User Manual, 

v 2.1, Federal Highway Administration, Washington, DC. Obtained from: http://www.

fhwa.dot.gov/infrastructure/asstmgmt/rc210704.pdf.

11. Federal Highway Administration. (1995). Pavement and Road Surface Management for 

Local Agencies, Participant's Manual, U.S. Department of Transportation, Washington, DC.

12. Federal Highway Administration. (1986). Highway Performance Monitoring System 

Analytical Process: Volume II, Technical Manual, Version 2.0, U.S. Department of 

Transportation, Washington, DC.

13. Florida Department of Transportation. (2008). Flexible Pavement Design Manual, Florida 

Department of Transportation, Tallahassee, FL.
54

14. Galehouse, L. and Sorenson, J.A. (2009). Quick Check of Your Highway Network Health,

Federal Highway Administration, Washington, DC. Obtained from: http://www.fhwa.dot.

gov/pavement/preservation/if07006ppt.cfm.

15. Geiger, D.R. (2005). Memorandum: Pavement Preservation Definitions, Federal Highway 

Administration, Washington, DC. Obtained from: http://www.fhwa.dot.gov/pavement/

preservation/091205.cfm.

16. Gronberg, G.D. and Blosser, N.B. (1956). “Lives of Highway Surfaces—Half Century 

Trends,” Public Roads, 29(2).

17. Haas, R., Hudson, W.R., and Zaniewski, J. (1994). Modern Pavement Management, Krieger 

Publishing Company, Malablar, FL.

18. Henry, J.J. (2000). Evaluation of Pavement Friction Characteristics: A Synthesis of Highway 

Practice, NCHRP Synthesis 291, National Cooperative Highway Research Program, 

Washington, DC.

19. Hicks, R.G., Simpson, A.L., and Groeger, J.L. (2011). Pavement Management Practices in 

State Highway Agencies: Madison, Wisconsin Peer Exchange Results, Federal Highway 

Administration, Washington, DC.

20. Hudson, W.R., Haas, R., and Uddin, W. (1997). Infrastructure Management, McGraw-Hill,

New York City, NY.

21. Kaplan, E.L. and Meier, P. (1958). “Nonparametric Estimation from Incomplete 

Observations,” Journal of the American Statistical Association, 53.

22. Lamptey, G., Ahamad, M., Labi, S., and Sinha, K. (2005). Life Cycle Cost Analysis of 

INDOT Pavement Design Process, Final Report, FHWA/IN/JTRP-2004/28, Indiana 

Department of Transportation, West Lafayette, IN. 

23. Lindqvist, B.H. (2006). “On the Statistical Modeling and Analysis of Repairable Systems,” 

Statistical Science, 21(4), 532–551.

24. Litzka, J., Leben, B., La Torre, F., Weninger-Vycudil, A., De Lurdes Antunes, M., Kokot, 

D., Mladenovic, G., Brittain, S., and Viner, H. (2003). The Way Forward for Pavement 

Performance Indicators Across Europe, COST Action 354 Performance Indicators for Road 

Pavements, European Cooperation of Scientific and Technical Research Institute for Road 

Construction and Maintenance, Brussels, Belgium.

25. National Institute of Standards and Technology. (2012). NIST/SEMATECH e-Handbook of 

Statistical Methods, Gaithersburg, MD. Obtained from: http://www.itl.nist.gov/div898/

handbook/. 

26. New York State Department of Transportation. (2010). Tappan Zee Bridge/I-287 

Environmental Review, Albany, NY. Obtained from: http://www.tzbsite.com/index.html.
55

27. Noyce, D.A., Bahia, H.U., Yambo, J.M., and Kim, G. (2005). Incorporating Road Safety into 

Pavement Management: Maximizing Surface Friction for Road Safety Improvements, 

Midwest Regional University Transportation Center, Madison, WI.

28. Paterson, W. and Chesher, A. (1986). “On Predicting Pavement Surface Distress with 

Empirical Models of Failure Times,” Transportation Research Record 1095, Transportation 

Research Board, Washington, DC.

29. Prozzi, J. and Madanat, S. (2000). “Using Duration Models to Analyze Experimental 

Pavement Failure Data,” Transportation Research Record 1699, Transportation Research 

Board, Washington, DC.

30. Rozycki, R. (2009). HPMS Reassessment: Pavements, Southeastern States Pavement 

Management and Design Conference, New Orleans, LA.

31. Shahin, M.Y. and S. D. Kohn. (1981). Pavement Maintenance Management for Roads and 

Parking Lots, Technical Report M-294s.l, U.S. Army Corps of Engineers, Washington, DC.

32. Snyder, M.B. (2006). Pavement Surface Characteristics—A Synthesis and Guide, American 

Concrete Pavement Association, Skokie, IL.

33. Song, W., Chen, X., Smith, T., and Hedfi, A. (2006). “Investigation of Hot Mix Asphalt 

Surfaced Pavements Skid Resistance in Maryland State Highway Network System,”

Transportation Research Board 85th Annual Meeting Compendium of Papers CD-ROM, 

Transportation Research Board, Washington, DC.

34. Speaks, S. (2010). Reliability and MTBF Overview, Vicor Reliability Engineering, Andover, 

MA. Obtained from: http://www.vicr.com/documents/quality/Rel_MTBF.pdf.

35. Stevens, L. (1985). Road Surface Management for Local Governments Resource Notebook,

Federal Highway Administration, Washington DC.

36. Texas Department of Transportation. (2008). Pavement Design Guide, Texas Department of 

Transportation, Austin, TX.

37. Vepa, T.S., George, K.P., and Shekharan, R. (1996). “Prediction of Pavement Remaining 

Life,” Transportation Research Record 1524, Transportation Research Board,

Washington, DC.

38. Wayson, R.L. (1998). Relationship Between Pavement Surface Texture and Highway Traffic 

Noise, NCHRP Synthesis 268, National Cooperative Highway Research Program, 

Washington, DC.

39. Wlaschin, B., Alam, M., and Gaj, S. Pavement Health Track Analysis Tool for Determining 

the Health of Pavement Networks, AASHTO Annual Meeting, Palm Desert, CA. Obtained 

from: www.transportation.org/sites/aashto/docs/Pavement%20Health%20TrackAASHTO.ppt.
56

40. Winfrey, R. and Howell, P.D. (1968). “Highway Pavements—Their Service Lives,” Highway 

Research Record 252, National Research Council, Washington, DC.

41. Winfrey, R. and Farrel, F.B. (1941). Life Characteristics of Surfaces Constructed on Primary 

Rural Highways, Volume 22, Public Roads Administration, Washington, DC.

42. Wisconsin Department of Transportation. (1996). Wisconsin Pavement Performance Report,

Madison, WI.

43. Witczak, M. (1978). Determination of Flexible Pavement Life, Report No. FHWA/MD/R79/1, Federal Highway Administration, Washington DC.

44. Yang, J. (2008). “Development of Effective Remaining Life Prediction Methods for 

Pavement Management at the Network Level,” Transportation Research Board 2009 Annual 

Meeting CD-ROM, Transportation Research Board, Washington DC.

45. Yu, J., Chow, E., and Yau, J. (2008). “Estimation of the Effects of Influential Factors on 

Pavement Service Life with Cox Proportional Hazards Method,” Journal of Infrastructure 

Systems, 14(4), American Society of Civil Engineers, Reston, VA.

46. Yu, J. (2005). Pavement Service Life Estimation and Condition Prediction, University of 

Toledo, Toledo, OH.

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