a

Use of Vehicle Noise for 

Roadways, Bridge, and 

Infrastructure Health Monitoring 

The Exploratory Advanced Research Program

WORKSHOP SUMMARY REPORT • AUGUST 20-21, 2013
b

Notice

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

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

contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names 

appear in this report only because they are considered essential to the objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

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

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

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

ensure continuous quality improvement.

Cover photo illustration: 

© Shutterstock, Web Design Hot!
i

Technical Report Documentation Page
ii
iii

Table of Contents

Introduction

Panel Discussion

Topic: Asset Management Current Practices, Key Issues, 

and Near-Term Advances

Day 1 Presentations

Presentation 1: What Can We Learn From Vehicle 

Noise and Vibration?

Presentation 2: Seeing Below the Surface Using 

Acoustic-Based Sensing and Imaging

Presentation 3: Using Vehicles as Sensors for 

Roadways, Bridge, and Infrastructure Health Monitoring 

in Connected Vehicles

Questions and Answers: Day 1

Day 2 Presentations

Presentation 1: Structural Health Monitoring Using New 

Technologies

Presentation 2: Decision Support System for RemoteSensing and Geographic Information System Technologies

Facilitated Discussion

Research Directions

Conclusions

Appendix A: Agenda

Appendix B: Workshop Participants

1

4

5

8

9

12

15

17

20

21

26

30 

31

33

37

39
iv

List of Figures

Figure 1. The versatile onboard traffic-embedded roaming sensors 

(VOTERS) test van.

Figure 2. Longitudinal road profile.

Figure 3. The contactless impact-echo scanning system.

Figure 4. Effective presentation of impact-echo data with transparency.

Figure 5. Fully contactless scanning system.

Figure 6. Highway Performance Management System traffic data.

Figure 7. Data uses and analysis and processing proposed system.

Figure 8. Microphone placement.

Figure 9. 24-GHz millimeter-wave radar array.

Figure 10. Pavement debonding detection.

Figure 11. Metal characterization example.

Figure 12. Multiple sensor confirmation.

Figure 13. Pavement monitoring system visualization.

Figure 14. Three-dimensional optical bridge evaluation system.

Figure 15. Bridge deck three-dimensional model.

Figure 16. Spall analysis.

Figure 17. Bridge deck delamination by thermal infrared and chain drag.

Figure 18. The decision support software interface.

9

11

12

13

14

15

16

21

22

23

23

24

24

26

27

27

28

29
v

List of Acronyms and Abbreviations

General Terms

3D three-dimensional

4D four-dimensional

AASHTO American Association of State Highway and 

Transportation Officials

ABS anti-lock braking system

DAS data acquisition system

DOT Department of Transportation

DSS decision support system

DTPS dynamic tire pressure sensor

DUAP data uses analysis and processing

EAR Exploratory Advanced Research

ESRI Economic and Social Research Institute

FHWA Federal Highway Administration

GIS geographic information system

GPR ground penetrating radar

GPS Global Positioning System

HPMS Highway Performance Monitoring System

IR infrared

IRI international roughness index

LIDAR light detection and ranging

LTBP Long-Term Bridge Performance

MASS mobile acoustic subsurface sensing

MDOT Michigan Department of Transportation

MTD mean texture depth

MWR millimeter-wave radar

NBI National Bridge Inventory

NDE nondestructive evaluation

NHS National Highway System

NVH noise, vibration, and harshness
vi

PAVEMON pavement monitoring

PCA principal component analysis

PCI pavement condition index

RABITTM Robot-Assisted Bridge Inspection ToolTM

R&D research and development

RD&T research, development, and technology

TFHRC Turner–Fairbank Highway Research Center

VIDAS vehicle-based information and data acquisition system

VOO vehicles of opportunity

VOTERS versatile onboard traffic-embedded roaming sensors 
vii
viii
1

Introduction

On August 20–21, 2013, at the Turner–

Fairbank Highway Research Center 

(TFHRC) in McLean, VA, the Federal 

Highway Administration’s (FHWA) Exploratory 

Advanced Research (EAR) Program convened 

a 2-day workshop entitled, “Use of Vehicle 

Noise for Roadways, Bridge, and Infrastructure 

Health Monitoring.” The objectives of the 

workshop were to discuss the possibilities 

of using vehicle noise for roadways, bridge, 

and infrastructure health monitoring and to 

use a noise-based data collection system 

that could assess infrastructure for proactive 

and efficient infrastructure maintenance and 

operations, higher infrastructure safety, and 

less traffic congestion.

Michael Trentacoste, Associate Administrator 

for Research, Development, and Technology 

(RD&T) and Director of TFHRC, welcomed 

workshop participants. Trentacoste introduced 

the subject of the workshop and encouraged 

participants to think beyond the technology 

that is currently available for the transportation 

industry. He also asked participants to consider 

several themes during the workshop, including 

how to collect data in a cost-effective manner, 

how the data would be integrated for analysis, 

and if there are any technologies that are being 

applied in other fields that might be applicable 

for this discussion. 

Joe Peters, Director of the Office of Operations 

Research and Development (R&D), then 

highlighted some of the current issues 

facing the industry. These issues include 

congestion, safety, and the inspiration 

for the workshop, reactive maintenance. 

Peters also emphasized the need for a 

delivery system to send informational 

messages from damaged infrastructure and 

encouraged participants to think beyond 

technologies that are available today.

Next, Cheryl Richter, Assistant Director for 

Pavements Research and Development of 

the Office of Infrastructure R&D, described 

her vision of a future in which the need for 

infrastructure damage alert systems would 

be eliminated by performing preventative 

maintenance at the appropriate time. 

This type of system would include 

technologies that assess infrastructure so 

that aging infrastructure could be addressed 

appropriately. 

David Kuehn, EAR Program Manager, provided 

an overview of the EAR Program and described 

the focus areas for this discussion. Topics 

included connected highway vehicle systems, 

technology for assessing performance, and 

information sciences. 

Mohammed Yousuf, who is with FHWA’s 

Office of Operations R&D, offered participants 

some perspective on how far technology 

has come in the past 100 years, specifically 

within the automotive industry. He also 

provided an overview of how much data 

are available today and explained the 
2

importance of asset management. Yousuf 

proposed using noise, vibration, and harshness 

(NVH) as a metric to study roadways, bridges, 

and infrastructure by creating infrastructureaware vehicles that act as mobile sensors and 

by leveraging emerging technologies and 

existing vehicle systems. Yousuf also noted that 

advanced data-processing methods need to be 

developed to interpret the data. He went on to 

provide several examples of using embedded 

microphones in vehicle systems, which included 

tire-pressure monitoring, wheel sensors, and 

infotainment systems. Yousuf explained that, 

after the noise data collection, using a series 

of sophisticated processing techniques could 

provide alerts for specified criteria for a given 

scenario or condition. In summary, Yousuf noted 

that potential impact of creating such a system 

could lead to new methods of asset-based data 

collection. In addition, with new forms of data 

available, gathered by using passenger vehicles 

as probe sensors, further discoveries could lead 

to other tools that would benefit the field, and 

supervisory site visits for asset management 

could be eliminated.

Shane Boone at the Office of Infrastructure 

R&D provided key background information 

on FHWA’s nondestructive evaluation 

(NDE) activities and the Long-Term Bridge 

Performance (LTBP) program. He said a 

reliable NDE method that bridge owners have 

been using for the last 40 years to evaluate 

bridge delamination and debonding is chain 

drag.

12 Boone explained that, although chain 

drag is inexpensive, it cannot be performed 

at highway speed; thus a lane must be 

closed, which causes congestion. Boone 

encouraged participants to consider how this 

type of measurement could be performed at 

highway speeds, what frequencies would be 

required, what properties of the materials 

could be evaluated, and how this process 

could be automated in the future. 

1 For more information on chain drag, visit http://

onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-

R06A-RR-1.pdf
3
4

Panel Discussion
5

Goals

Panel members began by discussing goals for 

using vehicle noise as a measure of infrastructure 

health. The following considerations were 

agreed upon:

• Detecting defects. The ultimate goal is to 

detect defects, such as potholes, before 

they become issues.

• Evaluating at speed. Researchers need to 

evaluate materials at highway speed, which may 

preclude physical contact with the materials.

• Calibrating messages and alerts. Collecting 

both acoustic and location data from many 

passenger vehicles in real time allows 

for calibration of location messages and 

degradation alerts.

• Specifying a threshold. Data should be 

collected all the time but only reported at 

a specified threshold.

• Understanding degradation and deterioration.

Continual data collection will allow for trending 

over time and a better understanding of 

degradation and deterioration. 

The panel then discussed the needs for the 

facilitation of these goals as outlined in the 

following sections.

Integration

The panelists highlighted that interaction 

between research and practice can result 

more quickly in infrastructure managers 

who can manage maintenance with 

minimal interruption.

Data Management

To process the large quantities of data 

collected by these systems, the panelists 

suggested first identifying how to sort 

through the data so that the information 

will be most useful. They also noted that the 

main purpose of collecting the data is to 

predict when materials need to be replaced. 

Technology Needs

Current Technology

The panel members initially highlighted 

the the Robot-Assisted Bridge Inspection 

ToolTM (RABITTM), an FHWA LTBP robot that 

integrates real-time acoustic, optical, radar, 

and other sensors to gather information 

about the condition of bridges while it travels 

along them. This tool provides near real-time 

information about the condition of a concrete 

bridge deck, and the precise location and 

Topic: Asset Management Current Practices, 

Key Issues, and Near-Term Advances

Panelists 

Shane Boone, Office of Infrastructure Research and Development

Dr. John Popovics, University of Illinois

Jack Stickel, Alaska Department of Transportation
6

nature of the damage.21 This was noted by 

the panelists as a step in the right direction.

Panel members then discussed current 

features on vehicles with the potential to 

be used for these goals. For example, active 

suspension is one feature that could indicate 

the density of the materials by evaluating the 

damping properties of the material. 

Other sensors that could be used in 

conjunction with acoustic sensors include 

optical sensors to visually confirm anomalies 

identified in data, depending on the quality 

and price point of cameras. During discussion, 

the panel members noted that infrared 

(IR) cameras can remove shadow from the 

images. According to the panelists, Illinois 

Department of Transportation uses a van 

with optical cameras to document pavement 

status. The panelists also highlighted that 

the use of ground penetrating radar (GPR) 

frequencies required for appropriate data 

collection may not be available for private 

or commercial fleet vehicles because of 

interference with other entities.

In summary, panelists discussed tracking the 

location of vehicles. They highlighted that, 

although commercial or maintenance vehicles 

could use differential Global Positioning 

Systems (GPS) so that their position in lanes 

are known, passenger vehicles would be more 

likely to use GPS found on mobile phones. 

This is because the cost of differential GPS to 

determine position, which cannot currently 

meet the required level of accuracy.

2 For more information, visit http://www.fhwa.dot.gov/

research/tfhrc/programs/infrastructure/structures/

ltbp/ltbpresearch/rabit/index.cfm

New Technologies

According to the panelists, the Alaska 

Department of Transportation and Public 

Facilities is exploring several new technologies, 

as follows:

•	 Light detection and ranging (LIDAR) 

and digital imaging and how to integrate 

with other operations.

•	 Road weather and traffic cameras.

•	 Maintenance decision support system 

(DSS).

•	 Side-fire radar for traffic volume.

•	 Quasi-real-time crash data.

•	 Geographic information system (GIS) 

capabilities.

o The Economic and Social Research 

Institute (ESRI) is developing a new 

roads-and-highway application for 

linear referencing.

•	 Expanded collaboration and data 

sharing using ESRI’s ArcGIS tool online.

o The American Association of 

State Highway and Transportation 

Officials’ (AASHTO) technology 

implementation group collaborated 

with the University of Connecticut to 

develop a program called “UPlan” (visit 

http://tig.transportation.org/Pages/

UPlan.aspx for more information).

o Remote data collection and remote 

power sources.

o Remote power supplies.

•	 Looking at fuel cell technology.
7

•	 A brand new system by Q Technology 

that uses natural gas or propane.

o FHWA’s Road Weather Management 

Program.

•	 Weather-responsive traffic 

management.

•	 Strategies for road weather changes.

Asset Management

Known Efforts

Each State is required to develop a riskbased asset management plan for the 

National Highway System (NHS) to 

improve or preserve the condition of the 

assets and performance of the system 

(23 U.S.C. 119(e)(1), MAP-21 § 1106). The 

asset management plan for the NHS 

includes data collection, maintenance, 

preservation, and integration. Additional 

subtasks include developing a base map 

of all public roads with a linear referencing 

system that will be integrated with the 

Transportation for the Nation dataset.

Recommended Resources

Panelists recommended the following 

additional resources:

•	 Transportation Research Board. 

(2013). AASHTO Transportation Asset 

Management Guide: A Focus on 

Implementation (FHWA Publication No. 

FHWA-HIF-10-023). Washington, DC.

•	 Transportation Research Board. (2010). 

National Cooperative Highway Research 

Program Report 666: Target-Setting 

Methods and Data Management to 

Support Performance-Based Resource 

Allocation by Transportation Agencies. 

Washington, DC.

Gaps and Research Areas

Panelists suggested that integrating 

and linking all different asset classes 

and transportation information—by 

creating an enterprise road network 

with a linear referencing system—would 

be one approach to further research. 

One area noted for improvement is 

developing a governance framework 

within agencies. Other critical issues 

that require attention include data silos, 

integration, and establishing common 

definitions. Panelists suggested that a 

useful strategy would be to “collect once, 

use many times.”

Current Research

Transportation Research Board research 

topics regarding asset management include 

the following:

•	 Expansion of capability maturity model.

•	 Roadway safety data partnership.

•	 Real-time system operation.
8

Day 1

Presentations
9

J

. Gregory McDaniel’s presentation 

focused on versatile onboard trafficembedded roaming sensors (VOTERS) 

that use vehicles of opportunity (VOO) as 

inspection data platforms. The research 

project that McDaniel described in the 

presentation has been ongoing for 5 years at 

Northeastern University.

McDaniel explained how the VOTERS 

project uses a test van that monitors road 

materials in real time. As shown in figure 

1, the van is equipped with the following: 

GPS, camera, directional microphone 

behind the left rear tire, laser height 

sensor, dynamic tire pressure sensor 

(DTPS), rear-axle accelerometer, a fivesensor surface millimeter-wave radar 

array, and a power supply. The van also 

features a portable real-time monitoring 

system, control station, and data acquisition 

and processing station. 

McDaniel provided an overview of how the 

VOTERS test van measures the surface 

waves in the road that carry information 

Presentation 1: What Can We Learn From 

Vehicle Noise and Vibration?

J. Gregory McDaniel

Boston University

Figure 1. The versatile onboard traffic-embedded roaming sensors (VOTERS) test van.

© Boston University 
10

about subsurface conditions. Waves in the 

tire wall carry information about the road 

surface from road and tire interaction. A key 

point that McDaniel noted during the 

presentation was that the surface waves in 

the road have very large wavelengths that 

reach the width of the entire road. It is 

therefore not necessary to measure exactly 

over an area with a subsurface issue to 

detect the issue. 

Specific challenges noted for these 

measurements include wind, vehicle 

vibration, and acoustic resonances. Key 

resonances to consider include the following:

•	 Engine rotation: 100s of Hz (100s of s-1).

•	 Vehicle bounce against springs and 

suspension: 1–2 Hz (1–2 s-1).

•	 Standing waves between the road and 

bottom of the car when integers of 

one-half wavelengths are present: 100s 

of Hz (100s of s-1).

•	 Air in tire will resonate acoustically.

Principal Component Analysis

McDaniel explained to workshop participants 

how mounting the directional microphone 

behind the rear tire eliminates wind noise. 

Processing data by using the principal 

component analysis (PCA), developed for 

this purpose, filters out the remaining noise. 

The vehicle speed must be known to 

complete the PCA analysis to output the 

mean texture depth (MTD). This method has 

been validated, and calibration can be 

performed by driving on a road with a 

known MTD. An exterior microphone can 

also be used to find the MTD at three 

different vehicle speeds with this system. 

Dynamic Tire Pressure

During the presentation, McDaniel noted 

that the DTPS concept measures the 

change in internal air pressure through a 

sensor mounted on the valve stem. This 

sensor can be added to any tire, powers 

itself, wirelessly transmits data, and will 

sample at 40 kHz. If the car is not moving, 

the sensor measures zero. He also noted 

the DTPS has been validated against an 

accelerometer on the ground.

McDaniel put forward to workshop 

participants several advantages to using 

DTPS, as follows:

•	 Ambient noise is no longer an issue, 

because the tire provides a barrier and 

approximately a 90-percent reduction 

in noise.

•	 Acoustic resonances involving the air 

in the tire act as a natural amplifier of 

ground motion.

•	 Interior pressure is a highly coupled to 

ground vibration through the adiabatic 

gas law. 

•	 Measurements can be made at typical 

roadway traffic speed. 

As shown in figure 2, if the DTPS is paired 

with an axel accelerometer, the impedance 

of the road can be measured to detect 

subsurface damage. This can also be 

performed at typical traffic speed, and the 

difference between the body of the car 

moving up and down and bumps in the 

road can be differentiated. McDaniel noted 

that this method can provide reliable 

correlations to a longitudinal road profile, 

pavement condition index, and international 

roughness index. 
11

Sensing Surface Waves

For an acoustic-sensing method for 

detecting surface waves, McDaniel told 

workshop participants that it is 

recommended to put the microphones as 

close to the ground as possible (without 

touching the ground). This method includes 

the use of a mechanical excitation cart to 

create the waves. Because the signal-tonoise ratio is very poor when using this 

method, an iterative wave-fitting method 

algorithm is currently in development. This 

algorithm estimates the complex number 

of the wave, and an important output of 

this algorithm is the decay rate. McDaniel 

also explained that the decay rate contains 

information on the properties of the 

material and whether there could be 

subsurface damage. 

Conclusion

In summary, McDaniel explained that all 

the methods presented are based on 

physics. He also highlighted that exploiting 

physics can provide significant advantages 

in noisy environments.

Questions

Following the presentation, McDaniel 

responded to the following question from a 

workshop participant: 

•	 Are temperature changes accounted 

for in the tire?

McDaniel confirmed that this is not the case 

because the time window during which the 

DTPS is used is very small, and the calculation 

assumes no transfer of heat energy.

Figure 2. Longitudinal road profile.

© Boston University
12

Presentation 2: Seeing Below the Surface Using 

Acoustic-Based Sensing and Imaging

John S. Popovics

University of Illinois

J

ohn S. Popovics’ presentation focused on 

cost-effective and accurate inspection 

of concrete transportation infrastructure. 

Popovics presented specific methodology to 

workshop participants, including scanning 

impact-echo for delamination detection 

and air-coupled ultrasonic surface wave 

characterization of defects.

Scanning Impact-Echo for 

Delamination Detection

Popovics began with an explanation of the 

impact-echo technique, a test based on 

impact resonance. The material is 

interrogated with a hammer, or ball drop, 

and the material responds by “ringing” in 

different stretch or flexural resonance 

modes, or a combination of modes. This 

allows for the identification of defects.

Popovics explained that flexural modes are 

easy to detect acoustically, and stretch 

modes are difficult to detect. In accordance, 

this method works well for near-surface 

delamination because it is characterized by 

flexural modes; however, a problem with the 

current methodology is that it requires 

detailed interrogation of the material and 

cannot be performed at highway speeds.

Popovics proceeded to describe the 

development of a contactless echo-scanning 

system that uses a microphone array. This is 

an inexpensive method that uses a rugged 

test setup. As shown in figure 3, a handdriven impact produces the needed 

excitation. Popovics also provided 

experimental results from a static system 

and a platform that moves at walking speed. 

Modal analysis of the results produces 

detailed images for fixed-testing systems; 

however, this type of analysis is not practical 

for bridge decks, because the goal is to 

collect data with a moving platform.

To analyze data from a moving platform, the 

system collects individual spectral signals 

that are stacked and mapped. A fourdimensional (4D) plot can provide a view of 

a thickness-stretch mode, as shown in figure 

4. Popovics suggested that the use of 

transparency simplifies the interpretation 

and clearly indicates where defects occur. 

For example, low-amplitude data are 

transparent (indicating no defect) and highamplitude data are opaque (indicating a 

defect). This is considered a very effective 

way of filtering data to view defects, and 

Figure 3. The contactless 

impact-echo scanning system.

© University of Illinois at Urbana-Champaign
13

This could be an inexpensive technique, 

because each sensor set (consisting of a 

transducer and receiver pair) costs about 

$70. The propagation source and receiver 

sensors can also be applied from a range of 

heights from the surface, but in this example 

they were 5 cm (2 inches).

This method allowed for the introduction 

of a fully contactless scanning frame, as 

shown in figure 5. According to Popovics, 

one of the most important features of a 

contactless scanning frame is the signal 

consistency. This allows for repeatability, 

regardless of surface roughness. In addition, 

this system can also measure energy 

attenuation to monitor surface distress. 

Popovics explained that using Lamb waves 

(plate waves) as the signal allows for a new 

method to detect the presence of 

delamination. By using the multichannel 

analysis of surface waves approach—

involving one sender with many receivers—a 

delamination can be identified very quickly, 

with potential for eventual application at 

highway speeds. 

when validated against other more 

personnel- and time-intensive methods, the 

4D image produces the same end result.

Popovics also provided an overview of 

testing the nonmoving platform on an 

active bridge. The project research team 

collected data on the bridge deck in 90 

minutes with a 100-percent hit rate. This 

meant that everywhere the analysis 

indicated there was a delamination, one 

was found. The presence of delamination at 

a subset of locations was determined from 

extracted core samples. Popovics noted 

that this method did not require filtering of 

signals to remove noise from active traffic, 

even though the roads were being heavily 

used during the measurement.

Air-Coupled Ultrasonic Surface Wave 

Technique

During his presentation, Popovics also 

covered the concept of using air-coupled 

ultrasonic waves to measure surface waves. 

Figure 4. Effective presentation of impact-echo data with transparency.

© University of Illinois at Urbana-Champaign
14

Questions

At the end of the presentation, Popovics 

responded to the following question from a 

workshop participant:

•	 Can this method be used to differentiate 

between different flaws?

Popovics confirmed that, although the 

location of flaws with flexural resonances can 

be detected, the exact flaw depth is unknown.

Figure 5. Fully contactless scanning system. 

(PC = personal computer, MEMS = microelectromechanical system.)

© University of Illinois at Urbana-Champaign
15

Presentation 3: Using Vehicles as Sensors for 

Roadways, Bridge, and Infrastructure Health 

Monitoring in Connected Vehicles

Lee Mixon

Mixon Hill

Data Uses Analysis and Processing

Mixon highlighted a project performed for 

Michigan Department of Transportation 

(MDOT), which included connected-vehicle 

data use, increased data sharing, and 

support performance management. The 

data uses analysis and processing (DUAP) 

system collects a large volume of data, 

including weather, infrastructure, mobile, 

fleet, freight, and construction data in a 

map-based system, as shown in figure 6.

Researchers for the project queried all 

organizational functional areas and 

Lee Mixon’s presentation focused on how 

agencies manage and operate systems 

and data. Mixon explained that agencies 

need highly accurate and reliable information. 

This type of information includes baseline 

information characterizing the assets, 

current condition information, information 

that puts the agency in control of managing 

and operating the infrastructure, and also 

systems that give the agencies information, 

control, and the ability to deliver automated 

results. The ultimate goal is to provide 

enough information so that agencies know 

what needs to be fixed and what materials 

are needed without traveling to each site first. 

Figure 6. Highway Performance Management System traffic data.

© Mixon Hill
16

subareas to understand and prioritize the 

data needs. In addition, Mixon reminded 

workshop participants of the importance of 

data distribution and the concept of “collect 

once, use many” (shown in figure 7). During 

the presentation, Mixon highlighted that 

multiple efforts to obtain the same 

information occur far too often. The DUAP 

system includes pavement, traffic arterials, 

weather, maintenance-user-delay cost, 

incident management, construction, 

financial, and other information.

Vehicle-Based Information and 

Data Acquisition System

Mixon explained that the vehicle-based 

information and data acquisition system 

(VIDAS) collects data as employees drive 

maintenance and supervisory vehicles. VIDAS 

then transmits the information in real time 

and processes and updates data in the user 

interface. Sensors that are used in this system 

include weather, accelerometery, LIDAR, and 

radar. The system is scalable to allow for 

growing data collection needs.

Figure 7. Data uses and analysis and processing proposed system.

© Mixon Hill
17

Questions and Answers: Day 1

•	 Can you tell the type and severity of 

the defect?

At the surface, it is possible to differentiate 

between different types of defects. 

Examples of detectable defects include 

potholes, cracks, and patches. In addition, 

video can provide visual confirmation.

•	 You are enhancing what the dedicated 

vehicle can capture. Are you moving 

away from using passenger vehicles to 

capture the data?

There is a calibration station, but this is in the 

research stage, so calibrations must be done 

and then compared to the current condition. 

Both passenger vehicles and dedicated 

vehicles could be used in the future.

Presentation 2: Seeing Below the 

Surface Using Acoustic-Based 

Sensing and Imaging

•	 Are the air-coupled sensors available off 

the shelf? Do they have analog inputs?

The microphones are available off the shelf. 

The brand is SHURE, the model is SM58, 

and the cost is approximately $80. The 

microelectromechanical system sensors 

are off the shelf, but the circuitry is not 

included and must be set up. These sensors 

produce analog signals.

•	 What are the expected speeds for each 

technique?

The impact-echo technique likely will never 

occur at highway speed, but it does work 

very well around 8–16 km/h (5–10 mi/h).

Following the presentations, workshop 

participants and presenters participated 

in a question-and-answer session. The 

participant questions and presenter responses 

are summarized below.

Presentation 1: What Can We Learn 

From Vehicle Noise and Vibration?

•	 Why not use a special tire?

The research team does not get to choose 

the source, although the team members 

would like to try a studded or solid tire. 

Chain drag was tried, but there were safety 

concerns. In addition, each tire is a source 

and receiver at the same time. The research 

team is working on an array to have four 

inputs and four outputs to capture any 

defect between the four tires.

•	 Have you been able to determine the 

thickness of materials?

The thickness of materials has been 

determined, and the differences in 

subsurfaces can be detected by using the 

DTPS, although it has not been codified at 

this point. When you start having delamination 

in roads, resonances occur, and that can be 

seen in the dynamic tire pressure. 

•	 On a standard tire, how deep can you 

see into the material?

It is possible to explore 91 cm (36 inches) 

with a standard tire.
18

•	 Are there geometric constraints for 

microphone placement?

There are geometric constraints. A closely 

spaced pair is used, and they interrogate 

specific places.

•	 Is there potential to merge the ideas 

presented in the first and second 

presentations?

There is overlap that would be good to explore.

Presentation 3: Using Vehicles as 

Sensors for Roadways, Bridge, and 

Infrastructure Health Monitoring in 

Connected Vehicles.

•	 Is there an off-the-shelf version of 

VIDAS? Do you think we would need 

a dedicated system, or can there be 

real-time processing?

VIDAS is a data acquisition system (DAS) 

and the goal for it is to create a set of 

vehicles that have a DAS that can have 

sensors added to it. The DAS must have a 

configuration data manager, use standard 

code, and when new sensors are added, 

they must be evaluated offline before being 

added to the overall platform.

An alternative approach would be to have 

each sensor coupled with its own 

processing power. Multiple computers 

could run on different operating systems, 

with the same positioning information that 

could communicate with each other 

through one main controller that manages 

the systems. Data could be automatically 

uploaded when the network is detected, 

although certain systems (e.g., GPR) could 

not fit in that system because of extreme 

DAS requirements.

•	 How big is the circuitry for these systems?

It can vary in sizes. The components are off 

the shelf, but multiple systems are 

distributed. In addition, when thinking 

about outfitting passenger vehicles, 

another sensor will need to be added on to 

sense subsurface.

General Questions

•	 Do we know now what sensor we 

need to include in future vehicles? 

This idea would not be a nearterm project, so what might now 

be considered expensive could be 

greatly reduced in cost with time and 

thorough collaboration.

Research needs must be defined and 

communicated with manufacturers for 

them to accept the ideas and make them 

possible. Combining research efforts with 

manufacturers has increased over the past 

few years.
19
20

Day Two

Presentations
21

Presentation 1: Structural Health Monitoring 

Using New Technologies

Ming L. Wang

Northeastern University

location, delamination, rebar corrosion, 

pavement thickness, moisture, and voids. In 

addition, acoustic sensors are used to detect 

debonding, voids, stripping concrete, 

potholes, MTD, and international roughness 

index (IRI). Optical sensors are used to view 

the surface profile, rutting, shoving, cracking, 

and potholes.

The acoustic system includes tire impact as 

the source and two different types of sensors. 

One sensor inside the tire measures the 

variation in tire pressure at high sampling 

rates. The other sensor is a microphone 

behind the tire, as shown in figure 8; however, 

Wang noted this system is complex, so the 

researchers also designed a simplified system 

to apply to a passenger or service vehicle. 

Wang explained that, by using the physicsbased system as proposed in the VOTERS 

acoustic system with the rear-mounted 

microphone, asset owners can calculate the 

condition assessment with Weibull 

Ming L. Wang’s presentation focused 

on the project, “Versatile Onboard 

Traffic-Embedded Roaming Sensors.” 

The goal of the project is to create a system 

that can be outfitted on cars that would 

lead to less congestion during infrastructure 

monitoring and maintenance.

Wang began by explaining that at present, 

infrastructure asset owners generally 

visually inspect a small percentage of the 

road every 2–5 years. Most monitoring is 

performed on the surface of the material, 

although monitoring the subsurface 

condition would be more effective in 

preventative maintenance. 

The VOTERS project provides a framework 

and sensor systems that would be carried 

by cars to shift from periodic, localized 

inspections to continuous network-wide 

health monitoring of roadways and bridge 

decks at traffic speed. VOO, such as buses 

and post office vehicles, could also be used 

to collect surface and subsurface roadway 

and bridge deck condition information at 

traffic speed. The collected data would 

then be transferred to a control and 

visualization center for further analysis, 

visualization, and decisionmaking. 

Wang explained that the VOTERS system 

uses millimeter-wave radar (MWR; 24 GHz) 

to detect surface conditions, such as water, 

ice, potholes, surface roughness, and rutting. 

Subsurface radar (2 GHz) is used to detect 

subsurface conditions, such as rebar 

Figure 8. Microphone placement.

© Northeastern University
22

in the data. Wang highlighted that the 

VOTERS PCI has been compared and 

validated against the traditional PCI in the 

field. Although the traditional PCI reports 

one value between intersections, VOTERS 

can report one value for smaller stretches 

of road at the order of 3–20 m (10–65 ft), 

depending on the vehicle speed.

The DTPS measures the longitudinal road 

profile, and materials can be identified 

based on the response. In addition, the IRI 

can be derived by the DTPS road profile and 

compared with the American Society for 

Testing and Materials’ standard for IRI. Wang 

noted that to evaluate subsurface, 

researchers tested 25 different pavement 

designs at the National Center for Asphalt 

test track and detected pavement 

debonding as a change in frequency, as 

shown in figure 10. 

Wang proceeded to explain to workshop 

participants how, by using the millimeter 

array, the transverse and longitudinal 

probability density function, variance of 

PCA signal, IRI, and MTD. Using the DTPS, 

the system can interpolate the longitudinal 

profile, IRI, and subsurface condition. In 

addition, the video inspection allows for a 

visual inspection of crack density, crack 

type, crack severity, bleeding and raveling, 

and condition assessment.

Wang noted that using the tire as a sensor 

alone is not sufficient for pothole detection, 

because most people try to avoid potholes 

when driving; however, the MWR addresses 

this concern. As shown in figure 9, it is an 

array that can detect rutting by evaluating 

the transverse profile, identify the surface 

material, and allow for condition assessment 

with radar longitudinal profile.

The VOTERS system can output a 

pavement condition index (PCI) equivalent 

rating by fusing collected data from the 

different sensors in near-real time. 

Moreover, the video can be used as a 

secondary opinion to confirm what is seen 

Figure 9. 24-GHz millimeter-wave radar array.

© Northeastern University
23

interrogate every meter of the roadway at 

walking speed. The impact source is also 

important, for example, if using a high, wideband frequency, the material can be 

evaluated around 1-m (3-ft) deep; however, 

if the impact is short and a high frequency is 

used, the material can only be evaluated 

very close to the surface.

Wang explained that the system described 

in this presentation uses a frequency below 

100,000 Hz and a 113 kg (250 lb) impact and 

that the depths and modal elasticity of the 

materials can be extracted from the 

collected data. The MASS vehicle system is 

a noncontact sensing system that uses 

microphones, mechanical and software 

noise filtering, and fast-processing 

algorithms that processes data in roughly 

2–3 seconds during collection.

Gen-3 Ground Penetrating Radar 

System

According to Wang, the GPR system can 

inspect at 80–96 km/h (50–60 mi/h) and 

consists of an array of 8 to 10 channels that 

are small enough to fit under a car chassis or 

can be hand-held. Wang explained that the 

development of the antennae was a large 

focus of this effort, and after many iterations, 

the research team selected an antennae 

directions are measured and types of issues 

and materials can be identified, as shown in 

figure 11.

Wang also noted that the sensors used in 

the VOTERS system confirm and 

complement each other to identify types of 

issues and materials, as shown in figure 12.

Detailed Inspection 

During the presentation, Wang explained 

that it is necessary to conduct a detailed 

inspection for areas with a lot of degradation. 

In events like this, a service vehicle with 

more robust equipment can inspect these 

locations, for example, a mobile acoustic 

subsurface sensing (MASS) vehicle could 

Figure 10. Pavement debonding detection.

© Northeastern University

© Northeastern University

Figure 11. Metal characterization example.
24

could be visualized with the GIS-based 

pavement monitoring (PAVEMON) system. 

The system also uses colors to represent 

road condition and tracks the condition 

every 3 m (10 ft). As shown in figure 13, the 

data is overlaid on a map provided by 

Google MapsTM. Wang noted that the 

PAVEMON visualizations can be compared 

and validated against historic PCI ratings of 

surveyed streets.

Lifecycle Analysis

According to Wang, asset owners typically 

conduct a lifecycle analysis by developing a 

pavement performance curve during the 

design phase to approximate lifetime costs 

and a repair schedule. The VOTERS system 

can monitor conditions more frequently to 

determine position on the performance 

curve during the lifetime, allowing the repair 

schedule to be updated and optimized. 

Wang noted that pavement deterioration 

called Pacman Bowtie. By using two Pacman 

antennae together as a transmitter–receiver 

pair, the researchers can evaluate depth, 

height, and property of the subsurface 

conditions. In addition, the GPR can detect 

areas of corroded rebar shown by comparing 

the reflection amplitudes to half-cell potential 

measurements. Wang noted that statistical 

analysis of GPR rebar amplitudes allows for 

immediate deck condition results.

Pavement Monitoring System

The VOTERS project also focuses on system 

integration to allow for full automation. 

Wang explained how, after fusing the data 

and integrating the data into the system, it 

Figure 12. Multiple sensor confirmation. 

(Accl = acceleration, DTPS = dynamic tire pressure sensor, 

LHS = laser height sensor, MWR = millimeter-wave radar.)

© Northeastern University

© Northeastern University

Figure 13. Pavement monitoring system visualization.
25

Questions

At the end of the presentation, Wang 

responded to the following questions from 

workshop participants:

•	 Has the use of a microphone on each 

tire been considered, instead of the 

millimeter-wave radar, to get the same 

resolution?

Wang confirmed a microphone can be used 

behind each of the tires. Moreover, there is 

also the potential and existing capability to 

outfit a regular passenger car with a DTPS 

system or with a concealer microphone 

array underneath the car. Wang explained 

that the MWR is designed to be a very 

inexpensive system that identifies other 

properties, such as potholes, routing, and 

surface materials. Therefore, one cannot 

replace the other—they are complementary.

•	 Has a standard vehicle been considered 

for the dynamic sensors?

Wang confirmed that this sensor is specifically 

designed to measure only the variation of tire 

pressure (dynamic) and can be modified for 

use on a regular car.

modeling was used to estimate repairs and 

lifecycle costs. By holding the PCI at 77, the 

research team found that using the VOTERS 

system and sealing cracks resulted in 

substantial cost savings. Wang summarized 

the overall VOTERS vision as being able to 

inspect and repair in the right time, right 

place, and right way. 

VOTERS Deployment Options

Wang outlined several deployment options 

during the presentation, as follows:

•	 Many single vehicles, widespread 

deployment, single channel, levelone sensors.

•	 Fleet vehicles, widespread deployment, 

arrays of level-one sensors.

•	 Service vehicles, dedicated and 

targeted surveys, arrays of level 

one and two sensors.

Wang also defined the following sensor 

levels during the presentation:

 1. DTPS, microphone, accelerometer, MWR.

 2. Level one, plus GPR array and video.
26

Colin Brooks’ presentation focused on 

the integration and display for DSS. 

Workshop participants were given an 

overview of the project, “Bridge Condition 

Assessment with Remote Sensors.” Brooks 

explained that remote sensing is the collection 

of data about an object, area, or phenomenon 

from a distance with a device that is not in 

contact with the object. This can be thought 

of as another form of NDE. Technologies used 

in this project include three-dimensional (3D) 

optics, thermal IR, digital image correlation, 

and ultra-wide band radar.

Three-Dimensional Optical Bridge 

Evaluation System

Brooks explained that the idea was to 

take high-resolution and overlapping 

photographs to create a high-resolution 3D 

model of the bridge deck to evaluate 

spalling, cracking, and other issues. In 

accordance, the research team mounted a 

camera to a frame 3 m (10 ft) above the 

bridge deck. As shown in figure 14, this 

camera was used to collect images of the 

bridge deck as the vehicle it was attached 

to maintained a speed of 3 km/h (2 mi/h) 

to collect images that overlapped by 60 

percent. The 3D data was accurate to 2 mm 

(0.08 inches) in the X, Y, and Z directions.

Brooks informed workshop participants 

about a new version of this project called 

“NDE Bridge Decks at Near-Highway Speed.” 

Presentation 2: Decision Support System for 

Remote-Sensing and Geographic Information 

System Technologies

Colin Brooks

Michigan Tech Research Institute

The investigators are currently developing a 

system that operates at a minimum of 72 

km/h (45 mi/h) and will run simultaneously 

with the thermal IR camera from Talon 

Research’s BridgeGuard system. The system 

will process images using the program, 

AgiSoft Photoscan, to create a digital 3D 

model of the bridge deck and integrate 

images to produce a 1-mm (0.039-inches) 

bridge deck photo (shown in figure 15).

Figure 16 shows a selection of images 

produced from the automatic spall analysis, 

compared with the actual spall location on 

the pavement. Brooks explained that the 

algorithm used in the spall detection analysis 

is programmed in Python and uses ArcPy to 

utilize ArcGIS geospatial tools. The focal 

statistics tool is used to find potential spalls, 

shapefiles (a geospatial vector data format) 

© Michigan Technological University

Figure 14. Three-dimensional optical bridge 

evaluation system.
27

are generated for detected spalls, and a 

table is generated with spall sizes and 

volumes. Brooks noted that the computation 

time for this analysis can be reduced if a 

spall-size threshold is included. 

Brooks explained that the research team 

also used thermal IR technology to find 

delaminations based on local temperature 

deviations and additional measurement 

methods, including chain drag. The thermal 

imaging and chain-drag method both 

found defects, but they did not always find 

the same defects. In figure 17, the red areas 

are the defects that were detected by the 

thermal IR method, and the green areas are 

the defects that were found by using the 

chain-drag method.

The research team also used other remotesensing technologies in this study, 

including remote-camera photo inventory 

with high-resolution location-tagged photos, 

LIDAR, satellite and aerial photos, 

interferometric synthetic aperture radar, 

and speckle image processing.

© Michigan Technological University

Figure 15. Bridge deck three-dimensional model.

Figure 16. Spall analysis.

© Michigan Technological University
28

Integrating Remote-Sensing

Brooks informed workshop participants 

that the goals for integration include a 

comparison of remote-sensing observations 

to established measures, a comparison of 

remote-sensing observations to one another, 

and a way to derive established measures 

from remote-sensing observations.

Existing Decision Support Systems

Brooks proceeded to outline a selection of 

existing decision support systems, as follows:

Pontis AASHTO BRIDGEWare System

Over 45 agencies throughout the United 

States and abroad have adopted this system. 

It represents bridges as sets of structural 

elements and supports optimization and 

asset management workflows. One gap in 

this system is that agencies cannot integrate 

remote-sensing NDE data directly in a 

geographic environment at this time.

Michigan Bridge Reporting System 

This system is a prototype for MDOT that 

integrates remote-sensing NDE data into 

MDOT’s existing data. 

Online Long-Term Bridge Performance National 

Bridge Inventory

The LTBP program is researching sensor 

data integration.

Asset Management

During the presentation, Brooks explained 

that components of the asset management 

decision process include previous bridge 

inspection reports, visual inspection, remote 

sensors, the National Bridge Inventory (NBI) 

rating and Pontis condition, decisions on 

maintenance, and bridge inspections. A 

bridge deck surface rating could provide a 

comparison to the NBI rating to assist with 

decisions on maintenance. Moreover, IRI can 

also be calculated from the data.

Software

Brooks explained that the decision support 

software is open source, can be viewed in 

any browser, and can access all inventory 

data. In addition, through the interface, 

users can access typical features, as shown 

in figure 18. These include existing inspection 

data, sorted by region or county, and 

additional sorting features, such as driving 

directions and NBI deck ratings. Remote-© Michigan Technological 

University

Figure 17. Bridge deck delamination by thermal infrared and chain drag.
29

sensing data is integrated, and users can 

click on a bridge and view directions, 

remote-sensing data, IRI ratings for each 

lane, the percent spalled, the percent 

delaminated, crack density, and images. 

Brooks noted that the main challenge with 

this system is the way bridge data is not 

stored with a focus on integrating the data 

rapidly into an online mapping tool.

Questions

At the end of the presentation, Brooks 

responded to the following questions from 

workshop participants:

•	 What limits optical resolution? Is it 

vibration?

Brooks explained that the vehicle speed 

relative to frame rate must be considered to 

minimize motion blur.

•	 When compared over a period of 

time, do different camera-mounting 

positions matter?

Brooks confirmed that the key is to have a 

base layer that all other images will use as a 

reference.

•	 Could the system work on unpaved 

roads?

Brooks suggested that this could perhaps 

work, but the investigators had not 

considered it.

© Michigan Technological University

Figure 18. The decision support software interface.
30

Facilitated Discussion
31

Following the presentations, workshop 

participants and presenters took part in 

a facilitated discussion. A summary for 

each topic of discussion is provided below.

Data Needs

During the discussion, workshop participants 

suggested that multiple data needs can be 

addressed. For example, MDOT is creating a 

fleet to constantly analyze the material 

conditions and also to collect weather and 

traffic conditions.

Participants also discussed how trending 

data over time will allow for learning about 

degradation over time and what factors 

contributed to the degradation. In addition, 

monitoring and trending of data can lead to 

a better understanding of actionable 

information, which ties into the DSS. In 

summary, criteria for actionable information 

must be defined now and could be improved 

over time.

Equipment Needs

During the discussion, workshop participants 

noted that having precise geolocation 

information is key for data integration—this is 

not something commercial vehicles currently 

have. Some participants suggested that an 

inexpensive GPS unit’s accuracy could be 

improved toward 3 m (10 ft) by using 

triangulation; however, triangulation is not 

considered a reliable method because 

satellites are turned off intermittently. 

Participants noted that this method could still 

have potential if bad data are thrown out, or 

by referencing data to a base layer that has 

very high resolution. Moreover, stitching 

together overlapping acoustical data from a 

known location could provide precise location 

data, although this would not be in real time.

Another equipment need discussed included 

the concept of how using multiple passenger 

cars provides many measurements of the 

same segment. A participant noted that hard 

points, such as manholes or bridge joints, 

provide references.

A possible starting point, put forward 

during discussion, is to explore existing 

passenger and service vehicle controllers. 

A workshop participant suggested that it is 

likely that existing controller sampling rates 

are much lower than what acoustic data 

require. Existing features, such as anti-lock 

braking systems (ABS), could be explored 

further since the technologies are similar in 

the sense that they are both looking for 

variations between tires.

As part of this discussion, participants 

stated that new technologies that could 

be added to vehicles should be explored 

now. For example, the DTPS could be 

installed on any commercial vehicle and is 

very robust. Participants also noted that 

likely solutions may be found through a 

combination of technologies.

Research Directions
32

Asset Needs

Workshop participants identified two levels 

of needs during this discussion, as follows:

•	 Reporting infrastructure conditions and 

performance.

•	 Management needs for maintenance 

planning.

Participants noted a common reporting 

metric that can be measured at high- or 

low-vehicle speed is desired. At present, 

high-speed IRI measurements are the only 

industry standard, but this is only applicable 

for a subset of roads. Distress data, collected 

at slow speeds, are not as repeatable as IRI 

measurements, have limitations, and are 

subjective. Several participants highlighted 

that the driver for this common metric is to 

reduce reactive maintenance. One potential 

solution put forward during discussion is to 

use a tire pressure sensor to provide IRI or 

to develop an empirical model to correlate 

PCI and IRI. A participant noted that PCI 

should also be improved because it only 

evaluates the surface of a material. For 

subsurface information, a map could be 

produced from sensor data to show the 

probability of a defect under the surface. 

Then, issues can be prioritized at a local 

level for maintenance. 

Calibration

During discussion, participants highlighted 

that calibration can be performed while 

driving. Participants noted that data sets 

can be scaled based on something that is 

known along the course. Bi-annual or annual 

calibrations could be performed by using a 

short test strip. One participant noted that if 

sensors are applied on all four tires then, 

during calibration, all four tires should read 

the same. In summary, participants noted 

that fixed and mobile sources of data could 

lead to quality checking. Fixed-point sources 

already exist and just need to be integrated 

into the system.

Evaluation Processes and Weather 

Considerations

Participants suggested that pavement 

conditions will be evaluated for damage or 

degradation with results in near-real time by 

sensing changes in the road between the 

sensors. Participants noted that this process 

does not rely on comparing conditions to 

previous measurements; however, data can 

be used to observe trends over time. This 

means that factors, such as weather or 

seasonal variation, do not impact the process 

of detecting present damage or degradation.

Manufacturers

During the final discussion topic, participants 

said that engaging manufacturers will assist 

with successful acceptance of this concept. 

Participants noted that the key is to decide 

on equipment and data requirements, and 

then manufacturers can carry out the 

implementation. Participants also said that 

data needs should be discussed widely so 

that those in other fields, with different 

motivations for the same data, can assist 

with the acquisition. 
33

Participants came to several conclusions 

at the end of the workshop, which are 

outlined below. 

Measurement Considerations

•	 Technologies working together can 

provide information on asset conditions 

through data integration.

o Technologies include microphones, 

DTPS, electromagnetic (radar, LIDAR, 

optical, and IR), GPS, accelerometers, 

ABS, and elastomer actuators (smart 

shock and stability control). 

•	 Calibration will be an important 

consideration. This may include:

o Tire condition.

o Material and reference specimen. 

•	 Robust sensors will be important for 

a system.

•	 Sensor location and orientation will 

have an impact on measurement.

•	 Spatial- and temporal-resolution needs will 

influence system components and costs.

•	 There is a balance between many 

low-quality and few high-quality 

measurements, suggesting the following 

different design paths:

o A system using sensors of 

opportunity on passenger vehicles.

o A system using low-cost sensors and 

sensors of opportunity on fleet vehicles.

o Higher cost sensors that can be 

added to existing service vehicles 

used for asset assessment.

Decision Support Systems

•	 Actionable information is the purpose 

of the system.

o Agency-wide engagement is important, 

because different organizational elements 

have different needs.

o The system needs to define what 

should be collected.

o The system also needs to define what 

should be a trigger for maintenance 

or rehabilitation.

•	 Data analysis needs.

o Trend data and variations in the rate 

of change may be more important 

for making decisions than a point 

value on condition. 

o Asset assessment and budgeting 

frequently takes place annually or 

bi-annually.

Conclusions
34

o Providing a reference to historical 

data is important.

o Improved models may be needed to 

compensate for external influences.

•	 Fixed component.

o Trending of data.

o Statistical model.

o Connectivity and integration between 

fixed and mobile systems.

•	 Mobile component.

o Manufacture engagement (original 

equipment manufacturer, Tiers 1, 2, and 

3) is required for passenger vehicles. 

Asset Needs

•	 Reporting needs.

o Objective metrics to report (IRI, PCI, 

MTD, corrosion, and delamination). 

o High speed (highway) versus low 

speed (local roads).

o Surface versus subsurface (no 

comparable scale currently available).

•	 Management needs. 

o Data to lead to better decisions.

•	 “Define once, use many.”

•	 Proactive maintenance (intervene 

and preserve).

o Performance measurement and 

management.

Potential System Concepts

•	 New technology that can be included in 

dedicated vehicles used for measuring 

asset conditions.

•	 Low-cost technology that could easily 

be added to fleet vehicles.

•	 Use of existing sensors and network 

technology found on private passenger 

vehicles and mobile devices. 
35
36

Appendices
37

Appendix A: Agenda

Use of Vehicle Noise for Roadways, Bridge, and Infrastructure 

Health Monitoring Workshop

Turner–Fairbank Highway Research Center, McLean, VA

Tuesday, August 20, 2013

1–1:45 p.m. Welcome and EAR Program Overview

	 	 • Michael Trentacoste, Associate Administrator for RD&T and Director, 

Turner–Fairbank Highway Research Center

	 	 • Joe Peters, Office of Operations R&D, Federal Highway Administration

	 	 • Cheryl Richter, Office of Infrastructure R&D, 

Federal Highway Administration

	 	 • David Kuehn, EAR Program Manager, Federal Highway Administration

1:45–2:15 p.m. Introductory Remarks

	 	 • Mohammed Yousuf, Office of Operations R&D, 

Federal Highway Administration

	 	 • Shane Boone, Office of Infrastructure R&D, 

Federal Highway Administration

2:15–3:15 p.m. Panel Discussion: Asset Management Current Practices, Key Issues, 

and Near-Term Advances 

	 	 • Shane Boone, Office of Infrastructure R&D, 

Federal Highway Administration

	 	 • Dr. John Popovics, University of Illinois

	 	 • Jack Stickel, Alaska Department of Transportation

3:15–3:30 p.m. Break

3:30–4:30 p.m. Presentations 

	 	 • What Can We Learn From Vehicle Noise and Vibration? 

Dr. J. Gregory McDaniel, Boston University

	 	 • Seeing Below the Surface Using Acoustic-Based Sensing	 	 	

Dr. John Popovics, University of Illiniois

	 	 • Using Vehicles as Sensors for Roadways, Bridge, and Infrastructure

Health Monitoring in Connected Vehicles 

Lee Mixon, Mixon Hill

4:30–5 p.m. Questions and Answers

5 p.m. Day 1 Adjourn
38

Wednesday, August 21, 2013

8:30–9:45 a.m. Presentations

	 	 • Structural Health Monitoring Using New Technologies	 

Dr. Ming L. Wang, Northeastern University

	 	 • Decision Support System for Remote-Sensing and GIS Technologies	

Dr. Colin Brooks, Michigan Tech Research Institute

9:45–10 a.m. Break

10 a.m.–12 p.m. Facilitated Discussion: Research Directions

12–12:30 p.m. Recommendations, Questions, and Answers

12:30 p.m. Day 2 Adjourn
39

Appendix B: Workshop Participants

Ahearn, Meghan USDOT Volpe Center

Alton, Osman Federal Highway Administration, Office of Operations R&D

Barker, Alan Oak Ridge National Laboratory

Birken, Ralf Northeastern University

Boone, Shane Federal Highway Administration, Office of Infrastructure R&D

Brooks , Colin Michigan Tech Research Institute

Clayton, Dwight Oak Ridge National Laboratory

Eshbaugh, Bill Federal Highway Administration, Eastern Federal Lands

Halkyard, Terry Federal Highway Administration, 

Exploratory Advanced Research Program

Hastings, Aaron USDOT Volpe Center

Kuehn, David Federal Highway Administration, 

Exploratory Advanced Research Program

McDaniel, J. Gregory Boston University

Mixon, Lee Mixon Hill

Peters, Joe Federal Highway Administration, Office of Operations R&D

Popovics, John University of Illinois

Ratliff, Tammy Federal Highway Administration, Eastern Federal Lands

Richter, Cheryl Federal Highway Administration, Office of Infrastructure R&D

Sivaneswaran, Nadarajah Federal Highway Administration, Office of Infrastructure R&D

Stickel, Jack Alaska Department of Transportation

Sturgeon, Purser Southwest Research Institute

Trentacoste, Michael Federal Highway Administration, Associate Administrator for 

RD&T, and Director, Turner-Fairbank Highway Research Center

Triandafilou, Lou Federal Highway Administration, Office of Infrastructure R&D

Wang, Ming Northeastern University

Wise, Larry Federal Highway Administration, Office of Infrastructure R&D

Yousuf, Mohammed Federal Highway Administration, Office of Operations R&D
Office of Operations Research 

and Development

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-14-059

HRTM-30/08-14(1M)E