Final_Report_4.pdf

Identification of Effective Next Generation

Pavement Performance Measures and Asset

Management Methodologies to Support

MAP-21 Performance Management

Requirements

Contract Number: DTFH6115C00046

Final Report

Submitted to:

Federal Highway Administration

Guang Y. Zeng

Office of Acquisition and Grants Management

Office of Contracting Operations – Team B

1200 New Jersey Ave., SE, E65-101

Washington, D.C. 20590

Submitted By:

Fugro Roadware, Inc.

8613 Cross Park Drive

Austin, Texas 78754

Tel. 512-977-1800 Fax. 512-973-9565 www.fugroroadware.com

Authored By:

Sirous Alavi, Ph.D., P.E., PTOE

Nima Kargah-Ostadi, Ph.D., P.E.

Michael P. Tavares, P.E.

Jerome Daleiden, P.E.

September 15, 2016 http://www.fugroroadware.com/

Performance Measures and Asset Management Methodologies Final Report FHWA-DTFH6115C00046 September 15, 2016 www.fugroroadware.com i

Table of Contents

Table of Contents ........................................................................................................... i List of Figures ................................................................................................................ii List of Tables ..................................................................................................................ii

1.0 Introduction

1.1 Background

1.2 MAP-21 and Performance-Based Management

2.0 Task 1 – Identify or Conceptually Develop More Strategic Pavement Performance

Measure(s)

2.1 State of the Practice in Pavement Performance Measurement

2.2 Search for Next Generation Performance Measure(s)

2.3 Research Team Criteria for Selection of Next Generation Pavement Performance

Measure(s)

2.4 Next Generation Network Level Pavement Performance Measure Using Traffic Speed

Deflection Devices (TSDD)

2.5 TSDD related Performance Measure based on Normalized Deflection Variation

2.6 TSDD related Key Performance Indicator (KPI)

3.0 Task 2 – Methodologies to Guide Implementation of a Comprehensive Asset Management

Plan

3.1 Building Upon Findings of the NCHRP Project 08-91: “Cross-Asset Resource Allocation and the Impact on System Performance” and its MS Excel Tool Prototype

3.1.1 Incorporating Risk in the Tool Prototype

3.1.2 Opportunities to Improve the NCHRP 08-91 Tool Prototype

3.2 Examples of Other Available Analytical Tools

3.3 A Conceptual Framework for Cross-Asset Transportation Asset Management

3.3.1 Resource Allocation Problem Formulation

3.3.2 Multi-Objective Optimization

3.3.3 Quantifying Objective Functions

3.3.4 Scaling Objective Functions

3.3.5 Quantifying Constraints

3.3.6 Incorporating Uncertainty and Risk

3.3.7 Conceptual Framework for Cross-Asset Resource Allocation

3.3.8 Multi-Objective Evolutionary Algorithms

3.4 Organizational and Cultural Changes: Bottom-Up Analysis and Top-Down Integration

3.5 Visualization and Communication

4.0 Task 3 – Develop Work Plans for Future FHWA Projects to Validate Task 1 And Task 2

Outcomes

4.1 Work Plan for Task 1

4.2 Work Plan for Task 2

5.0 References

6.0 Bibliography

ii

List of Figures

Figure 1 – Types of Pavement Condition Data Collected

Figure 2 – Types of Pavement Distress Data Collected

Figure 3 – TSD and Diagnostic Checks

Figure 4 – RWD

Figure 5 – Cumulative Distribution Function for the NDVR Crack Initiation Thresholds Estimated for LTPP SPS-1 Sections in Oklahoma and Virginia

Figure 6 – Project Benefits from NCHRP Report 806

Figure 7 – Bottom-Up Approach to Optimization in the NCHRP 08-91 Tool Prototype. 22

Figure 8 –Top-Down Analysis for Optimization in the NCHRP 08-91 Tool Prototype. .. 22

Figure 9 – Asset Manager NT Overview from NCHRP Report 545

Figure 10 – Asset Manager PT Overview from NCHRP Report 545

Figure 11 – METIS Cross-Asset Tool Overview

Figure 12 – Example Framework for Using Multi-Objective Optimization in Asset Management

Figure 13 – Objectives and Constraints in Cross-Asset Resource Allocation

Figure 14 – Example Cross-Asset Multi-Objective Transportation Asset Management. 31

Figure 15 – Example Illustration of a Two-Objective Pareto-Optimal Front

Figure 16 – Quantifying Uncertainty in Objective Functions

Figure 17 – Conceptual Framework for Cross-Asset Resource Allocation Using Multi- Objective Optimization

Figure 18 – Example Visualization of the Final Pareto-Optimal Front for Multi-Objective Optimization

Figure 19 – Example Visualization of Multiple Performance Outcomes (Objectives) by Agency Strategic Direction

Figure 20 – Example Matrix Scatter Plots to Demonstrate Trade-Offs Among Multiple Objective Functions

Figure 21 – Example Visualization of the Impact of Different Investment Levels on the Multiple Performance Measures

List of Tables

Table 1 – Existing Pavement Performance Measures

Table 2 – Pavement Performance Measures Under Research and Development www.fugroroadware.com

1.0 INTRODUCTION

Fugro Roadware, Inc. (Fugro) is pleased to provide the final report for Federal Highway

Administration (FHWA) contract number DTFH6115C00046, entitled “Identification of

Effective Next Generation Pavement Performance Measures and Asset Management

Methodologies to Support MAP-21 Performance Management Requirements.”

The primary objectives of this Contract are to:

1. Identify, or conceptually develop, more strategic pavement performance measures, and their means of collection, in order to strengthen performance management and better link investments to long term performance. Consideration shall include how the data supporting the next generation method would be collected, analyzed, and presented and the potential costs and benefits of using this type of performance measure.

2. Identify, or conceptually develop, methodologies to enable full implementation of a comprehensive asset management plan, including trade-off analysis from a common ground among disparate assets that are traditionally individually assessed and managed.

The specific tasks identified in the project work plan are:

Task 1 – Identify or conceptually develop more strategic pavement performance

Measure(s).

Task 2 – Identify, or conceptually develop, methodologies to enable full implementation of a comprehensive asset management plan.

Task 3 – Develop work plans for future FHWA projects to validate Task 1 and Task

2 outcomes.

Task 4 – Develop and submit a final report to document the entire research effort.

This final report covers all the work activities accomplished under this contract.

1.1 Background

Infrastructure asset management refers to a data-driven decision support system that helps authorities in making decisions among alternatives for further development of assets or improvement of existing infrastructure. Increased infrastructure deterioration, increased performance demands, and challenges posed by climate change, along with limited budgetary and human resources have all made asset management a critical and challenging task. To address these challenges, the Moving Ahead for Progress in the

21st Century (MAP-21) legislation requires performance-based management of transportation systems and one of its national performance goals involves infrastructure condition. In addition, MAP-21 recognizes preservation by allowing funding eligibility for preservation projects. Under the new legislation, each state agency is required to develop www.fugroroadware.com a “risk-based asset management plan” to improve or preserve the condition of existing highway assets. These requirements are also carried forward into the current Fixing

America’s Surface Transportation (FAST) Act.

In order for a successful implementation of a performance-based management system, appropriate quantifiable performance measures and corresponding targets need to be established. Pavement condition on the Interstate System and non-Interstate National

Highway System (NHS), and bridge condition on the NHS, comprise three of the twelve performance areas identified by the United States Department of Transportation

(USDOT). FHWA is disseminating its proposed rulemaking in various areas by developing notice of proposed rulemaking (NPRM) documents. The NPRM process provides an opportunity for stakeholders including State Departments of Transportation

(DOTs), and Metropolitan Planning Organizations (MPOs) to provide their comments and feedback on the proposed procedures and measures. As of June 2016, FHWA has developed five NPRMs related to safety performance measures, highway safety improvement programs, planning, pavement and bridge condition performance measures, and asset management plans. Of primary interest to the objectives of this project are the FHWA NPRM on Asset Management Plan (RIN 2125-AF57)1 and FHWA

NPRM on National Performance Management Measures; Assessing Pavement Condition for the National Highway Performance Program and Bridge Condition for the National

Highway Performance Program (RIN 2125-AF53)2. The primary objectives of this project, which are the development of strategic pavement performance measure(s) and methodologies to enable full implementation of a comprehensive asset management plan, will need to complement the FHWA rulemakings and adhere to the MAP-21 performance-based asset management planning and programming.

MAP-21 has increased the awareness and involvement in transportation asset management (TAM) as the key system for performance-based management of highway infrastructure. MAP-21 defines TAM as “...a strategic and systematic process of operating, maintaining, and improving physical assets…that will achieve and sustain a state of good repair over the life cycle of the assets at minimum practicable cost.”3 Core principles of TAM are policy-driven planning, performance-based decisions, analysis of options and tradeoffs across alternative projects and assets, supporting decisions based on quality data and information, and providing transparent accountability and feedback through monitoring of the infrastructure network. FHWA plans to take advantage of the implementation of MAP-21 to increase the return of the public investment in our highway infrastructure by maximizing overall performance of the highway network while minimizing life cycle costs.

1.2 MAP-21 and Performance-Based Management

The performance-related provisions within MAP-21 have been categorized by FHWA into the following six groups:

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1. Establish National Goals to focus the Federal-Aid Highway Program on specific areas of performance.

2. Establish Measures (by FHWA) to assess performance and condition of assets.

3. Establish Targets (by State DOTs and MPOs) to document expected future performance.

4. Establish Plans (by State DOTs and MPOs) to achieve performance needs.

5. Develop Reports (by State DOTs and MPOs) to document progress toward achieving targets.

6. Establish Accountability through developing requirements (by FHWA) to assist

State DOTs and MPOs to achieve or make significant progress toward reaching performance targets.

The national goals were established in MAP-21 to focus on the following key aspects of the transportation network, which could be summarized as safety, mobility, and preservation goals:

Safety

Infrastructure Condition

Congestion Reduction

System Reliability

Freight Movement and Economic Vitality

Environmental Sustainability

Reduced Project Delivery Delays

MAP-21 requires state agencies to develop plans that will layout the strategic direction for identifying and addressing performance needs of their highway network. Plans ensure that each agency’s priorities are aligned with the national goals at strategic, tactical, and operational management levels. MAP-21 also requires biennial reports to the FHWA on the condition of the NHS and the effectiveness of investment strategies to provide feedback on the performance of the management program. This mechanism will demonstrate the method for implementation of available resources to the public, elected officials, and the stakeholders. Furthermore, MAP-21 has considered provisions to require state DOTs to undertake actions if the set performance targets are not met according to the plan for each state. These provisions encourage accountability of the transportation management system.

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2.0 TASK 1 – IDENTIFY OR CONCEPTUALLY DEVELOP MORE

STRATEGIC PAVEMENT PERFORMANCE MEASURE(S)

“Performance measure” is a relatively broad term that can encompass subjective and objective data to account for pavement distresses and surface characteristics that may influence pavement performance. Pavement performance measures have traditionally been related to metrics that describe physical characteristics of the pavement structure

(e.g., International Roughness Index (IRI), rutting, faulting, cracking percent). Physical characteristics of pavement structures have been measured by various data collection methods including visual condition surveys (automated, manual), non-destructive testing and also destructive testing. In order to identify more strategic pavement performance measures, one has to first understand the current status of pavement performance measures and define the need for more strategic pavement performance measures.

There are three very distinctive and important characteristics that define the nature and attributes of pavement performance measures. These are:

1. Project level performance measures versus network level measures - The need for performance measures at the project level are very specific as the objective is to identify the exact causes of pavement distresses to develop the most cost-effective maintenance/rehabilitation strategy. At the project level, the impact of data collection on the traveling public is contained and localized. Pavement condition data (both automated and manual) and non-destructive testing such as falling weight deflectometer (FWD) testing are frequently utilized for pavement performance data collection. Often times it is possible to conduct even more invasive destructive materials sampling and testing with proper advanced planning. At the network level however, pavement performance data collection often covers thousands of miles of agency roadway network. At the network level, the pavement performance data collection is often needed to provide critical input for development of performance models in support of prioritization of maintenance or rehabilitation efforts and funding. The impact of data collection on the traveling public in forms of delay and safety are major concerns. Therefore, routine pavement performance measures and evaluations at network level are often performed by the automated pavement distress/condition data collection equipment traveling at prevailing roadway speed. Other auxiliary information such as climatic and traffic data are also collected by devices that have no impact on user travel times.

2. Functional performance measures versus structural measures - From an agency point of view, pavements need to meet certain thresholds in functionality and structural adequacy in order to provide an acceptable Level of Service (LOS) to the traveling public. Pavement functional attributes are primarily based on ride quality and safety. Measurements of pavement smoothness and pavement/tire noise are examples of attributes that impact user’s perception of ride quality.

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Pavement performance measures associated with pavement texture (friction) and/or the propensity for hydroplaning are associated with safety. Structural performance measures such as deflection, layer thickness, materials properties and load related surface distresses are associated with the structural capacity of a pavement section. Pavement performance measures related to the structural capacity of a pavement section are used in conjunction with traffic loading and environmental data to determine the structural adequacy of an in-service pavement.

3. “Lagging” versus “leading” performance measures - The current proposed pavement performance measures for MAP-21 (i.e., IRI, cracking percent, rutting, faulting) are all lagging measures as they are based on metrics, which measure outcomes or results. Surface distresses are all reactive measures to a pavement investment (i.e., the existing roadway) that has already been made. On the other hand, measurements of pavement structural layer properties are leading indicators as they are based on metrics that measure input and can be used to predict and influence the desired output (e.g., adequately designed pavement structure to withstand loading at a desired LOS). Lagging indicators (output indicators) such as pavement surface distresses are relatively easy to measure as compared to leading indicators (input indicators) such pavement structural layer properties.

However, leading indicators are input oriented and can track potential performance issues before a problem arises, and thus are easier to influence compared to lagging indicators.

The following discussion on the current state-of-practice in pavement performance measures is an important gateway to the identification of next generation network-level pavement performance measures that are leading indicators of long term performance and influential factors in achieving minimum practical cost over a pavement’s life cycle.

2.1 State of the Practice in Pavement Performance Measurement

Obviously, the practice of network-level pavement performance measurement is different among various highway agencies. Table 1 shows the existing performance measures that are being collected and used in some form by various national or international agencies. Not all of the agencies are using all of the following measures and there are vast differences in the implementation methodologies among agencies.

In NCHRP Synthesis Report 401 on quality management of pavement condition data collection4, researchers surveyed 45 states and nine Canadian provinces regarding pavement management practices and found that more than 95 percent of these agencies collect network-level surface distress data and smoothness data. Furthermore, 34 percent collect network-level friction data and 16 percent collect network-level structural capacity data. Moreover, among those agencies that collect network-level distress data, more than half collect data on rutting, transverse cracking, fatigue cracking, longitudinal www.fugroroadware.com cracking, map/block cracking, raveling, faulting, spalling, and bleeding/flushing. Figure 1 and Figure 2 depict results from this survey.

Table 1 – Existing Pavement Performance Measures.

National Goal Objective Type Measure Typical Repair Strategy

Safety

Skid Resistance

Functional

Friction Number (FN)

Chip Seal, Micro- Surfacing, Thin Overlay

Sideways Force Coefficient (SFC)

Macro-Texture Depth

(MPD, MTD, SMPD)

Surface Bleeding/ Flushing Area

Avoid Hydroplaning

Functional

Rut Depth Micro-Surfacing, Thin Overlay, Mill & Inlay

Vehicle Handling

Gradient

Geometry Correction Cross-Slope

Curvature

Sight Distance

Infrastructure Condition

Provide Ride Quality

Functional International Roughness Index

Micro-Surfacing, Thin Overlay, Mill & Inlay

Surface Raveling/ Weathering Area

Chip Seal, Micro- Surfacing, Thin Overlay

Structural Health

Structural

Rut Depth Micro-Surfacing, Thin Overlay, Mill & Inlay, Mill & Overlay

Wheel Path Cracking Area

Overlay, Mill & Overlay FWD Deflection

Remaining Service Life

Load Transfer Efficiency

Dowel Bar Retrofit

Joint Faulting Diamond Grinding, Dowel Bar Retrofit

Pumping Area Localize Repair

Punchouts Patching

Potholes

Surface

Non Wheel Path Cracking Area

Crack Seal

Crack/Joint Spalling Crack/Joint Seal, Patching

Thermal Cracking Crack Seal

Map Cracking Area No Treatment

Figure 1 – Types of Pavement Condition Data Collected.

Figure 2 – Types of Pavement Distress Data Collected.

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Some key elements regarding pavement sustainability revolve around the need to keep smoother pavements smooth and a reduction in positive macro-texture without compromising safety. Both these elements contribute to reduced fuel consumption and greenhouse gas (GHG) emissions. Positive macro-texture also contributes to noise, which has both environmental and societal impacts. There are well established relationships between road surface condition (primarily roughness and macro-texture) and GHG emissions. For heavily trafficked roadways, the savings in GHGs incurred when keeping a road smooth are magnitudes greater than the GHG emissions generated in the maintenance operations (including both maintenance materials and traffic delays resulting from maintenance operations) needed to keep them smooth. This has to be an element in any asset management system. Similarly, long-lasting infrastructure often has a huge life cycle environmental benefit even if the initial environmental costs might be slightly higher. The use of life cycle assessment (LCA) is needed to establish environmental impacts.

The traditionally used pavement performance measures (e.g., IRI, cracking indices, composite pavement condition index, and rutting indices) do not necessarily reflect the immediate positive influence of applying preservation treatments. Nevertheless, the long-term benefits of timely preservation tend to manifest in decreased deterioration rates, which have not been fully studied for all existing performance measures. Past research

(NCHRP Report 5235) has validated the increased cost-effectiveness and performance benefits in the timely application of preservation treatments as opposed to a strategy of corrective maintenance and rehabilitation alone. However, most performance indicators are more sensitive to rehabilitation treatments and cannot be used for triggering preventive maintenance or preservation treatments. There is a need for evaluating the long-term effect of preservation on the deterioration rates of existing performance measures. Furthermore, there is a need for existing and new performance measures that reflect the impact of preservation treatments on the calculated service life and life cycle costs. The ongoing NCHRP Project 14-33 is aiming to develop such performance measures. This way the scenario analyses of pavement management systems will be equipped with the necessary tools to influence decision making towards strategic incorporation of preservation treatments.

2.2 Search for Next Generation Performance Measure(s)

Over the years several pavement composite indices have been developed to combine various structural and functional measures into one pavement performance measure.

The latest of these efforts was a FHWA study published in May 2013, which reported on the “Development of Next Generation Pavement Performance Measures” as part of

FHWA ongoing effort to improve FHWA’s ability to assess highway infrastructure health

(FHWA-HIF-042).6 The objective of the study was to develop a “next generation pavement performance measure that provides an accurate and repeatable assessment of the functional condition of the roadway.” The aim was to solely rely on the Highway

Performance Management System (HPMS) database and develop a combined measure www.fugroroadware.com based on ride quality, cracking, rutting and faulting. At the initial stages of the report, it became apparent that pavement performance measures describing the physical conditions of a pavement (i.e., rutting, cracking, faulting) provide “very different information” about the condition of a pavement as compared to functional measures (e.g., IRI). Ride quality measures provide information on the user’s perception of pavement condition, while physical performance measures provide specific information about the physical condition of a pavement. A decision was made to change the course of that study to evaluate rutting, faulting, cracking and ride quality as four separate pavement performance measures using the HPMS database. The study also concluded that consideration of pavement structural condition as a performance measure is “highly desirable” but there are technological deficiencies at the current time to collect such data nationally at the network level. For each of the four performance measures under study, the report also provided requirements for data collection, data processing, data QC/QA, data storage, and condition rating.

Moving ahead from these reactive measures, there is a need for strategic performance measures that assess the long-term impact of investment scenarios on infrastructure condition. Such indicators would aid TAM procedures to maintain pavements in a State of Good Repair (SGR), in compliance with the MAP-21 performance-based management concept.

Several data elements are critical to identify available performance measures that could be implemented towards more strategic measures. Condition or performance measures being considered by FHWA and SHAs are a logical starting point. Table 2 represents a sample list of numerous pavement performance measures under research and development by various agencies across the US and internationally.

2.3 Research Team Criteria for Selection of Next Generation

Pavement Performance Measure(s)

Pavement performance measures have traditionally been within one of the following three categories:

1. Pavement performance measures based on individual distresses (e.g., average rut depth, %of slabs that are cracked, etc.)

2. Pavement performance measures based on multiple distresses (e.g., pavement condition index, structural condition index, etc.)

3. Pavement performance measures based on deflection data (e.g., Indiana

Structural Strength Index, Texas Structural Condition Index, etc.)

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Table 2 – Pavement Performance Measures Under Research and Development.

National Goal Objective Type Measure Comments

Safety Skid Resistance

Functional

Simulated Macro- Texture Depth and Variation using 3D Imaging

Transverse MTD, MPD and RMS

Micro-Texture Only Stationary Devices

Infrastructure Condition

Provide Ride Quality

Functional

Road Impact Factor calculated using accelerometers in mobile devices

Independent of vehicle suspension system

Structural Health

Structural

Highway-Speed Deflection Data

Facilitates network-level continuous structural monitoring

Ground Penetrating Radar (GPR) Thickness

Current practice at project-level

GPR detected voids Current practice at project-level

Remaining Service Life/Interval

Current practice at project-level

Financial Considerations

Reduce Life- Cycle Costs

Not Applicable

Agency Costs

Functional Vehicle Operating Costs (User Costs)

Wear/Tear, Fuel, traffic congestion cost

Surface Rolling Resistance MIRIAM project

Economic Vitality

Functional TBD[a]

Cost of providing the highway level of service versus the provided economic opportunity

Environmental Impact

Reduce Impact

Surface Close Proximity Noise (CPX)

Affected by Macro-Texture

Functional

GHG Emissions Emissions from construction activities, user traffic, and end-of-life

Fuel Consumption Depletion of natural resources

[a]TBD: To be determined

The research team developed the following general criteria for selection of the next generation pavement performance measures in the current project:

1. For the performance metric and its measure to become practical and widespread at network level, an agency should be able to collect the pertinent data at or near highway speed with no interruption to traffic flow. Of course, the data required for the performance metric should not be cost prohibitive for the agency to collect.

2. The performance metric and its measure should be a leading indicator of pavement performance.

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3. The performance metric and its measure should have significant impact on the selection of performance enhancement strategies that are prescribed to preserve/extend the life of the pavement at minimized life-cycle costs.

4. The performance metric and its measure should have the potential to become fully developed technically, analytically, and economically practical within a 5-10 year time horizon.

As described earlier, pavement performance measures utilizing deflection data would provide a “leading” performance measure as it would result in valuable information with regard to pavement structural layers. At the project level, knowledge of deflection data is consistently used in structural evaluation to enhance the pavement management system

(PMS) decision making process. A recent FHWA study on “Simplified Techniques for

Evaluation and Interpretation of pavement Deflections for Network-level Analysis”

(FHWA-HRT-12-023, December 2013)7 states that the incorporation of structural analysis on PMS (in that specific study) changed the outcome of prescribed treatments on approximately 60 percent of study sections. This is another confirmation that consideration of pavement structural condition as a performance measure meets the research team criterion 3 above on the significant impact on selection of M& R strategy.

Deflection data, however, has been acquired by performing Falling Weight Deflectometer

(FWD) testing, which does not meet the research team criterion 1 above on being a practical test at the network level. As indicated in Figure 1, only a small percentage of agencies in the US collect structural capacity data at the network level. Hence, pavement management has historically lacked the structural capacity information needed to measure the remaining service life of pavements at the network level.

2.4 Next Generation Network Level Pavement Performance Measure

Using Traffic Speed Deflection Devices (TSDD)

Structural evaluations have historically been done only at the project level due to the high cost of collecting data and safety concerns regarding the lane closures during testing.

Agencies have been looking for an alternative approach to measure structural condition at the network level that can be reliable, cost-effective, and collected safely. Surface distress measurements alone are not enough to capture the actual rate of pavement deterioration and do not provide adequate information to develop optimal structural rehabilitation strategies. In addition, pavement preservation activities such as chip sealing or thin overlays may also temporarily hide signs of structural (load) related distresses such as fatigue cracking.

For over a decade, there has been continuous interest and technological advancement in developing equipment that can be used for structural evaluation of pavements at or near highway speed. Extensive literature is readily available on the use and evolution of various traffic speed deflection devices (TSDD). A number of studies including the

Second Strategic Highway Research Program, Project R06(F) and FHWA study on

Pavement Structural Evaluation at the Network Level (FHWA-HRT-15-074)8 suggest that www.fugroroadware.com the Traffic Speed Deflectometer (TSD) and the Rolling Wheel Deflectometer (RWD) are the two most promising devices at this time. The TSD (Figure 3) is an articulated truck with a rear dual-wheel axle of about 20,000 lbs. This device measures the velocity of the road surface resulting from the load applied by the truck. The RWD (Figure 4) has a series of displacement lasers mounted on a semi-trailer that collects nondestructive pavement deflections caused by an 18-kip single axle load. The deflection is calculated by comparing the laser scan profile as the RWD moves forward. Both RWD and TSD provide continuous measurements at intervals as small as a few inches. Both devices also allow for monitoring multi-lane and high traffic roads without the need to have extensive traffic control. This results in increased safety for road users and testing crews since no lane closures are needed during data collection. This also keeps traffic moving and reduces user delays.

Figure 3 – TSD and Diagnostic Checks.

Figure 4 – RWD.

A recent FHWA study has confirmed that TSD measurements provide a robust assessment of pavement structural conditions at the network level. The RWD device is also promising especially with improvements in the number of sensors and their locations

(FHWA-HRT-15-075)9. This FHWA study also shows that TSDD data can be utilized to develop promising deflection indices for further structural evaluation of pavements at the www.fugroroadware.com network level. For example, the study considered and developed a series of deflection indices (e.g., radius of curvature, deflection slope index, tangent slope, etc.) that exhibit a good correlation with critical pavement responses (in their case with maximum tensile strain at the bottom of asphalt concrete (AC) layer for fatigue cracking).

At the minimum, this network level structural evaluation is a screening tool to identify and locate weaker and inadequate sections and to estimate pavement rehabilitation or maintenance needs. It can also be integrated with other data collection devices such as

Ground Penetrating Radar (GPR) and an inertial profiler to significantly improve data collection efficiencies and reduce collection costs.

Of particular interest is the Transportation Pooled Fund study number TPF-5(282) titled

“Demonstration of Network Level Pavement Structural Evaluation with Traffic Speed

Deflectometer.” As a part of this study, TSD data was collected in nine participating States to demonstrate the use of TSD for network-level pavement structural evaluation. Among the primary objectives of this pooled fund study is the “exploratory analysis in the use of

TSD derived pavement structural parameters for use in the participating SHA’s pavement management system and applications.” This information is also expected to further research on the understanding of these measurements. The objective is to work towards an understanding of how the TSD can be used to estimate the structural condition

(adequacy) of a pavement given anticipated loading and environmental considerations.

2.5 TSDD related Performance Measure based on Normalized

Deflection Variation

In a paper presented at the 2016 TRB Annual Meeting, an alternative idea was presented to develop a network-level deflection-based leading performance measure22. This pilot study developed a new performance measure based on the variation of deflection across the length of each test section normalized to the variation in pavement thickness. The trends of the resulting performance measure with time was compared against the trends in cracking data in order to determine the crack initiation threshold for the new measure.

Pavement deterioration rate is not uniform across each pavement management section.

This is in part due to the initial construction variability in thickness, materials composition, and compaction of different pavement layers. Other sources of variability in structural integrity across pavement length include presence of water and drainage structures, dynamic loads due to surface roughness, proximity to other structures (bridges, culverts, etc.), lack of confinement (ineffective shoulder), and water intrusion due to joint/crack sealant issues and other surface openings. As a result of the variation in structural integrity along pavement sections, there are weaker areas that exhibit lower stiffness.

This localized lower stiffness is reflected by higher deflections measured on the pavement surface. Consequently, the variation in surface deflection along the pavement section increases with time from the initial variation caused by construction inconsistencies. In this preliminary study, the variation in surface deflection normalized to the average www.fugroroadware.com deflection value, which is the coefficient of variation (CoV) in deflection was proposed as an indicator of pavement deterioration.

The coefficient of variation in deflection reflects the initial variation in the deflection basin immediately after construction, and the increase in deflection variation as a result of non-uniform deterioration of the pavement section with time. In an effort to benchmark this normalized deflection variation index to the initial construction variability in pavement thickness, the CoV of the deflection basin was divided by the CoV of the total pavement thickness using ground GPR data. The resulting pavement structural index was called the “Normalized Deflection Variation Ratio (NDVR)”, which is calculated as follows:

𝑁𝐷𝑉𝑅 =

𝐶𝑜𝑉𝐷

𝐶𝑜𝑉𝐺𝑃𝑅

where

𝑁𝐷𝑉𝑅 = normalized deflection variation ratio

𝐶𝑜𝑉𝐷 = coefficient of variation of the entire deflection basin along the section

𝐶𝑜𝑉𝐺𝑃𝑅 = coefficient of variation of the total pavement thickness along the section

Since the highway speed deflection measurement devices collect pavement surface deflections at more frequent intervals, such continuous measurements (accompanied with GPR data) would be ideal to capture the full picture of variation in structural integrity across pavement length. However, due to the lack of adequate number of years with deflection data collected through these devices and the lack of accompanying distress data, this preliminary study was conducted using FWD data extracted from the FHWA’s

Long-Term Pavement Performance (LTPP) program.

For each test section, a sigmoidal function is fitted to the NDVR values, and to the progression of the combined cracking distresses initiated in the wheel path. The time of initiation of longitudinal or fatigue cracking in the wheel paths and on the pavement surface was estimated using the distress sigmoidal function for each test section and the corresponding value of NDVR at that point in time was estimated based on the fitted

NDVR sigmoidal function. The calculated NDVR at the time of crack initiation can be used as an estimate of the threshold which indicates potential cracking. Figure 5 shows the cumulative distribution function (CDF) for the crack initiation NDVR threshold values estimated on all LTPP SPS-1 test sections in the Oklahoma and Virginia sites. The NDVR threshold value corresponding to a CDF of 0.5 shows a mean of 19.3 for these test sections (the standard deviation of these NDVR thresholds is 11.1). In other words, when the coefficient of variation in the FWD deflection basin increases to be about 19 times the coefficient of variation in total pavement thickness, cracking can be anticipated within the wheel paths. This threshold range could potentially be used to determine if the pavement structure is in a good condition to trigger pavement preservation treatments. Of course, this number is based on the assumptions in this pilot study and further investigation using deflection data on other test sections is warranted to determine pertinent threshold values for this newly developed potential performance index.

Figure 5 – Cumulative Distribution Function for the NDVR Crack Initiation Thresholds

Estimated for LTPP SPS-1 Sections in Oklahoma and Virginia

The presence of some scatter in the NDVR values could lead one to conclude that the fitted sigmoidal function might not be a perfect representation of its trend. This scatter could be due to seasonal temperature and moisture differences between FWD test dates, also random measurement errors due to the FWD and GPR devices. Of course, there is also some scatter in the distress values which could be attributed to the subjectivity of measurement methods and the inherent variation in distress ratings. As a result of this scatter and lack of fit of the sigmoidal model, the coefficient of variation in the NDVR crack initiation threshold values is about 57 percent. With the future collection of continuous traffic speed deflection data along with GPR and distress data, larger data pools will be available and a statistical analysis can be conducted to determine the significance of the correlation between NDVR and crack initiation. In addition, with larger data pools, a better performance prediction model (similar to the fitted sigmoidal functions) can be developed for applications in pavement management systems.

2.6 TSDD related Key Performance Indicator (KPI)

As discussed before, structural evaluation of network pavements using TSDD provide the means for developing “leading” pavement performance measures. However, it does take more than TSDD measures (or TSDD driven deflection indices) to develop a “leading” performance indicator, which provides an assessment of the need for further rehabilitation versus maintenance/preservation forecasting strategies at the network level. This is because other important factors namely; pavement layer thicknesses, anticipated traffic loads, and environmental conditions all impact the remaining pavement life (or service interval). Therefore, the conceptual next generation pavement performance measure would be a KPI consisting of the following parameters:

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TSDD deflection measures (or deflection indices). There are ongoing FHWA research studies that have already proposed corresponding network-level deflection-based performance measures. The alternative conceptual performance measure based on deflection variation (discussed in section 2.5) should also be further evaluated for this purpose.

Anticipated traffic loading to the next service interval.

Consideration of environmental impact and variation (e.g., temperature, moisture).

Pavement structural layer thickness information from GPR readings.

Surface distresses and roughness are not included as they are captured by other NPRM

MAP-21 pavement performance measures. It is envisioned that the TSDD KPI would be used to compliment the current typical network PMS approach, which is based on metrics related to roughness and surface distress measures. It is also important to note that metrics chosen or combined erroneously can produce misleading indicators that yield incorrect and/or low performance measures. Inaccurate measures will produce bad management decisions.

The FHWA pooled fund study on demonstration of network level pavement structure evaluation with TSD, FHWA study on pavement Structural Evaluation at the Network

Level, and several other TSD data collection efforts nationally and internationally can be used for the development of the KPI. The project team is also aware of current efforts by the FHWA team to develop similar performance measures utilizing the pooled fund TSD data.

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3.0 TASK 2 – METHODOLOGIES TO GUIDE

IMPLEMENTATION OF A COMPREHENSIVE ASSET

MANAGEMENT PLAN

In recent years, State DOTs and regional agencies have been utilizing asset management plans and system performance measures to various degrees to manage their systems, evaluate project-level investment scenarios and transportation and land-use issues. With

MAP-21 legislation in place and the subsequent Fixing America's Surface Transportation

Act ("FAST Act"), States are required to develop a risk-based Transportation Asset

Management Plan (TAMP) for their NHS infrastructure. To facilitate this process, FHWA has published a NPRM for Asset Management Plan (RIN 2125-AF57)1. Among the main objectives of this NPRM is establishing the process for developing an asset management plan by a State DOT. Another important objective is to “establish the minimum content requirements of a State DOT asset management plan including minimum standards for developing and operating bridge and pavement management systems.” Some key attributes of the NPRM on Asset Management Plan are:

State DOTs are required to develop and implement asset management plans for the NHS relating to physical assets. At the minimum the asset management plan should encompass pavement and bridge assets.

State DOTs are required to include performance measures and targets for all assets included in their asset management plans.

Performance measures for bridges and pavements are the national measures established by FHWA (i.e., NPRM 2125-AF53)

Target values for performance measures are the desired future value of performance measures given the existing conditions, budget constraints and short and long term goals. Targets are set by State DOTs and are used to guide their resource allocations and program delivery.

The FHWA NPRM reaffirms State DOTs consensus that transportation asset management systems should be developed around performance-based decision making processes. AASHTO Transportation Asset Management Guide (2013)10 states: “an agency must be able to demonstrate that they are making progress on established goals and objectives and set goals and objectives tied to measurable metrics; make resource allocation decisions based on these goals and objectives and the funding available using the metrics to guide the decision making; and then demonstrate to its customers the results of the investments.”

According to the FHWA Asset Management Plan NPRM, there are six required processes for a State DOT Asset Management Plan:

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1. Conducting performance gap analysis and identifying strategies to close gaps – NPRM defines the performance gap as the gap between actual and desired condition and performance of the NHS.

2. Conducting life-cycle cost analysis for an asset or asset sub-group at the network level – The intended asset management life-cycle cost analysis is different than the life-cycle cost analysis in the design process as it includes the cost of managing an asset over its entire life. In this context, the life-cycle cost analysis is used to determine a strategic treatment plan (i.e., preservation, rehabilitation, and reconstruction along with routine and corrective maintenance) for an asset during its entire life while attempting to minimize costs and maintain or enhance the asset condition.

3. Incorporating risk management analysis in the asset management plan – The literature review conducted by Fugro’s research team revealed that there is a significant amount of misinterpretation of the concept of risk management analysis in the context of an asset management plan. The risk management analysis as intended by FHWA for a State DOT asset management plan is based on the measure of uncertainties in achieving the set performance targets given changes in current and future funding levels and unforeseen environmental events. The risk management plan will also include the process by which a State DOT manages the uncertainties in order to achieve its set performance targets. The risk management analysis will also take into consideration if reasonable alternatives exist for assets (i.e., roads, bridges) that repeatedly require treatment activities due to emergency events.

4. Developing a 10-year period financial plan – A State DOT will develop a methodology to determine the estimated costs of expected future work and estimated available funding for a 10-year financial plan. It is important to note that the definition of “work” does include actions to improve the physical condition of highways and bridges following the SGR concept, as well as to improve other aspects of their performance such as functionality and LOS.

5. Establishing a process to develop investment strategies – State DOTs are tasked to evaluate funding options and anticipated impact of those funding options on conditions and performance of NHS assets. The evaluation of various investment strategies will enable a State DOT to assess its progress towards achieving the States’ performance targets.

6. Using pavement and bridge management systems to analyze the condition of Interstate highway and non-Interstate NHS pavements and NHS bridges, and to determine optimal management and investment strategies – State

DOTs have been using various pavement and bridge management systems for their agency planning process for years and are familiar with data collection, processing, and analyzing schemes.

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Following the MAP-21 legislation directives and the FHWA NPRMs, the research team has created a conceptual framework for full implementation of a comprehensive asset management plan. The research team has also investigated other potential challenges with respect to implementation of TAMP, which include data availability and data quality.

In addition, due to the cross-asset nature of TAM data, pertinent governance and stewardship practices are very important to facilitate data access and update for all parties involved.

The major goal of a TAMP is to create strategies leading to a program of projects that would satisfy the national goals and the State specific set performance targets. Using the performance measurement concepts identified in Task 1 and the measures proposed by

FHWA NPRMs, the research team has considered a framework for addressing agency goals and objectives such as maximizing overall performance of a highway network, while minimizing life-cycle costs and environmental implications, and optimizing social impacts.

The following sections first describe the research team’s understanding and insight from the NCHRP 08-91 project. Building upon the findings of that project and other previous research, a conceptual framework is proposed for full implementation of a comprehensive asset management plan.

3.1 Building Upon Findings of the NCHRP Project 08-91: “Cross-

Asset Resource Allocation and the Impact on System Performance” and its MS Excel Tool Prototype

The objective of the NCHRP 08-91 research project was to provide a data-driven decision analysis tool based on a performance management concept.11 The project report provides a literature review that is representative of the state of the art regarding the various steps involved with such a data-driven analysis. In addition, the state of the practice in eight State DOTs was reviewed, but the focus was mainly on general characterization of the existing hurdles regarding cross-asset resource allocation.

Traditionally, State DOTs’ investment and resource allocation decisions have been based on geographical areas (i.e., districts) and/or program units (e.g., pavements, bridges, safety) with very little consideration of overall system-wide implications of resource allocation decisions. Under this traditional approach, project planning, project selection, and program development occur in program units and/or geographical areas in what is called a “siloed” approach. In addition, statewide transportation improvement program

(STIP) allocations are commonly based on historical distributions and funding restrictions rather than the achievement of…