RPUG 2022 Speakers Info

Session 0.1: TPF-5(399) Distresses Pooled Fund Meeting by Andy Mergenmeier, FHWA
Bio:
Andy Mergenmeier is a Senior Pavement and Materials Engineer with the FHWA. His primary responsibilities include pavement surface characteristics measurement and analysis, construction materials acceptance, and pavement construction. He is the FHWA liaison to the American Association of State Highway and Transportation Officials (AASHTO) Committee on Materials and Pavements Technical Section responsible for management of pavement measurement standards including pavement profiling, friction, rutting and cracking. He is managing the pooled fund study, TPF-5(299)/(399), Improving the Quality of Pavement Surface Distress and Transverse Profile Data Collection and Analysis, which he will be discussing this morning. Andy is the Field Project Engineer for the FHWA Friction Management Program project that includes demonstrating continuous friction measurement technologies within the framework of a friction management program. He came to this position in 2007 after 7 years as the state of Virginia’s Department of Transportation’s (VDOT) State Materials Engineer. At VDOT he was responsible for overseeing preliminary engineering and construction functions, such as, pavement design and construction materials acceptance and testing programs. Before VDOT, Mr. Mergenmeier worked for the FHWA for 15 years in various locations throughout the US.
Mr. Mergenmeier is a Civil Engineering graduate from the University of Kansas, and a Registered Professional Engineer.
Abstract:
Improving the Quality of Pavement Surface Distress and Transverse Profile Data Collection and Analysis – Pooled Fund Meeting

This meeting is for its members and all others interested in this subject.

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Session 0.2: Profiling 101 by Steve Karamihas, UMTRI
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This is an instructional presentation for personnel who are new to profiling or need to reinforce their fundamental background knowledge. It covers: (1) the basics of profile measurement, (2) common measurement errors, (3) ride quality basics, (4) International Roughness Index, (5) roughness profiles, and (6) a primer on filtering. The primer on filtering covers the basics of digital filtering in the context of road profile measurement and analysis. The primer includes an introduction to high- and low-pass filters, common filter types, filter frequency response, the use of filters to investigate sources of roughness, and the use of filters to investigate measurement issues.
Session 1.0: Moderator by Scott Mathison, Pathways (RPUG president)
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Session 1.1: Welcome from RPUG by Scott Mathison, Pathways (RPUG president)
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Session 1.2: Welcome and Keynotes from NYSDOT by Russ Thielke, NYSDOT
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Session 1.3: Vendors’ introduction by All vendors,
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Session 1.4: Session Break by ,
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Session 2.0: Moderator by Dave Huft, SDDOT
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Session 2.1: TPF-5(354) Pooled Fund Updates by Dave Huft, SDDOT
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TPF-5(354) Pooled Fund Updates
Session 2.2: Mitigation of Profile Measurement Errors During Braking and Stops by Steve Karamihas, UMTRI
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Inertial profilers can measure the longitudinal road elevation profile and the International Roughness Index (IRI) accurately when they are operated under favorable conditions. However, their performance deteriorates when they experience disturbances such as lateral and longitudinal accelerations and when the profiler host vehicle travels very slowly or comes to a complete stop. This presentation describes data processing algorithms that reduce profile measurement errors without adding sensors to the typical inertial profiler design. The method combines specialized processing algorithms with standard filtering techniques to mitigate artificial roughness caused by accelerometer drift and misalignment. The performance of the error suppression algorithms is demonstrated using several test runs collected under challenging conditions, including operation at low speed, braking, and operation through a stop. For all test runs, performance is quantified using standard measures for the accuracy of longitudinal profile and IRI used by road agencies for pavement network quality assurance and pavement network management. Since no additional hardware is required, these algorithms offer low-cost options for immediate implementation within the existing fleet. The algorithms do not offer a complete solution because they mitigate significant upward biases in roughness measured at stops at the cost of reducing the validity of the measured profile at low speed.

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Session 2.3: Zero-Speed Inertial Profiling System by Nicholas Schaefer, SSI and Brent Bergman, SSI
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Measuring accurate, certifiable longitudinal profiles and reporting reliable IRI results at low speeds and through the host, vehicle stoppages have long been a shortcoming of inertial profilers. Commercially available inertial profiling systems, relying on single-axis accelerometers, have required an actual forward speed to collect valid pavement profile data. Low speeds, speed changes, and host vehicle stoppages introduce well-known anomalies in the profile data during data collection. These limitations take away from the effectiveness of conventional inertial profilers, especially in urban area collections and under construction project conditions where fluctuating operating speeds and vehicle stoppages are often routine.
SSI has developed and commercialized a new “zero-speed” inertial profiler technology. With the enhanced instrumentation added to a standard inertial profiler, the zero-speed device can generate certifiably accurate and repeatable profile data (and IRI results) collected throughout random speed changes, vehicle stoppages, and without lead-in or lead-out data. SSI’s presentation will cover the development of zero-speed technology, with details about the hardware and software. We’ll also cover validation of the system, including end-user experiences, DOT certifications, and future developments needed to embrace the technology entirely.

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Session 2.4: Augmentation of Inertial Profilers for Operation During Braking and Stops by Steve Karamihas, UMTRI
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Inertial profilers can measure the longitudinal road elevation profile and the International Roughness Index (IRI) accurately when they are operated under favorable conditions. However, their performance deteriorates when they experience disturbances such as lateral and longitudinal accelerations, and when the profiler host vehicle travels very slowly or comes to a complete stop.
This presentation describes the use of additional sensors to improve profile measurement at low speed, during braking, and at stops. The augmented system includes inertial and GPS measurement of profiler kinematics. A multi-rate extended Kalman filter combines the inertial sensors with the GPS outputs to reduce drift and errors associated with profiler host vehicle tilt. Use of a Rauch-Tung-Striebel smoother improves the mitigation of drift. The performance of the augmented inertial profiler is demonstrated using several test runs collected under challenging conditions, including operation at low speed, braking, and operation through a stop. For all test runs, performance is quantified using standard measures for accuracy of longitudinal profile and IRI used by road agencies for pavement network quality assurance and pavement network management.
Use of inertial measurement in three dimensions and GPS measurement of profiler height and orientation is shown to give the best objective performance. The recommended processing algorithm integrates an additional mode of operation into the Kalman filter that applies an alternative measurement model at stops. Nearly equivalent performance was observed when the GPS outputs were replaced by artificial signals. This version of the system offers an option for accurate profile measurement in urban canyons.

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Session 2.5: How Ohio’s Interstate System Ride Quality Changed in 20 Years by Brian Schleppi, Retired from OH DOT
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In 2002, the OH DOT did a detailed ride quality investigation on its Interstate System from highway network inertial profile data collected during the 2001 season. This presentation will compare that 2001 “snapshot” to the 2021 “snapshot” in time to see how the network has changed from a ride quality perspective over the 20 years. It will look at the development and evolution of construction smoothness specifications, inertial profiler equipment and certification changes, construction practices, and other factors contributing to the observed changes. It will use the International Roughness Index (IRI) to quantify Ride Quality (smoothness/roughness) of the pavements, structures, and the Interstate System holistically.
Session 2.6: Practices for Ensuring the Smoothness of Concrete Bridge Decks During Construction by Rohan Perera, SME
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This presentation will summarize the findings from NCHRP Synthesis Topic 52-03, Practices for Ensuring the Smoothness of Concrete Bridge Decks. The objectives of this synthesis were to document the procedures used by state DOTs to evaluate the smoothness of concrete bridge decks when constructed, procedures used to keep track of the roughness of concrete bridge decks over time, and procedures used to maintain the smoothness of concrete bridge decks over their life. A survey was sent to the DOTs in the fifty states and the District of Columbia to gather study objectives. The methods used by state DOTs to evaluate the smoothness of newly constructed concrete bridge decks ranged from using only a straightedge, using a rolling straightedge, using data from a walking profiler, or an inertial profiler to perform a rolling straightedge simulation, based on IRI, and based on profilograph measurements. This presentation will describe some past studies performed to evaluate the smoothness of concrete bridge decks. This presentation will concentrate on presenting the IRI-based methods and profilograph-based methods used by State DOTs for evaluating the smoothness of newly constructed concrete bridge decks.
Session 2.7: Profiling Qs and As by All speakers,
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Session 2.8: Lunch by ,
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Session 3.0: Moderator by Kevin McGhee, VDOT
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Session 3.1: TPF-5(345/463) Pavement Surface Properties Consortium by Kevin McGhee, VDOT
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TPF-5(345/463) Pavement Surface Properties Consortium
Session 3.2: The use of Texture Measurements to Model Skid Resistance by Ahmad Alhasan, Applied Research Associates
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Dr. Ahmad Alhasan is a Senior Engineer and the Research and Technology Deployment Team leader at Applied Research Associates (ARA) Inc. Ahmad specializes in transportation infrastructure performance evaluation and management, pavement and geomaterials assessment and design, pavement surface characterization and its impacts on traffic safety, and statistical analysis for multi-attribute decision making under uncertainty.
Abstract:
Tire-Pavement friction is a complex phenomenon affected by multiple factors, including, but not limited to, the pavement surface texture, tire geometry, material characteristics, sliding speed, slip ratio, and skew angle. Previous studies and practitioners agree that pavement surface texture contributes to friction through two primary mechanisms: hysteresis and adhesion. Despite this agreement on the general understanding of the role texture plays in developing friction, there has been less agreement on the models to quantify the impact of surface texture on friction and the proper texture parameters to be used in these models. In this study, the team presented the findings from a study that used high-density laser texture scans to model the Locked Wheel Skid Trailer (LWST) results. The study included 20 pavement surfaces, and skid resistance was acquired using the smooth and ribbed LWST tires. Two cores from each pavement section were acquired and tested using the British Pendulum Tester (BPT) in the laboratory and scanned using the Ames LTS 9400HD with the highest density to capture the macrotexture and a portion of the microtexture. The team developed a simplified model based on the Persson friction model using the high-density texture scans. The presentation will introduce the concepts used in Persson’s friction model, the perturbation approach to simplify the model, and the experimental and modeling findings from this study. The surface texture was characterized using wavelets energy as an average representation of the power spectral density (PSD) within a given bandwidth. These energies were then correlated to the peak skid number (SN), the average SN, and the fully locked SN. The initial study findings are promising and show solid correlations and good model predictivity. The presentation will also discuss future improvements to include continuous friction measurement equipment.
Session 3.3: The Relationship between Network-level Friction and Texture on Kentucky’s Roads by Ryland Potter, WDM and Mike Vaughn, KYTC
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Ryland Potter has over 15 years of experience in senior strategy, management, and planning roles in both the public (Virginia, Texas) and private sectors. In 2018, she joined WDM USA to build awareness of continuous friction measurement and pavement friction management in the US. In this role, she works with Departments of Transportation to develop and implement pavement friction management programs and presents regularly on best practices in pavement friction management. Since 2021, Ryland has also chaired the American Traffic Safety Services Association (ATSSA) High Friction Surface Treatment (HFST) Council. Mike Vaughn is a Transportation Engineer Specialist for the Kentucky Transportation Cabinet (KYTC) within the Division of Traffic Operations at the Central Office in Frankfort, KY. In his current role, Mike leads a team of engineers who administer Kentucky’s Highway Safety Improvement Program (HSIP). Previous roles at KYTC include Highway Design Engineer, District 7’s Bridge Engineer, and statewide Value Engineering Coordinator. Mike is one of KYTC’s representatives on the AASHTO Committee on Safety. He is also KYTC’s representative on the Pavement Surface Properties Pool Fund Study – TPF-5(463). Mike is a graduate of the University of Kentucky with a bachelor’s degree in civil engineering. He is a licensed professional engineer in the state of Kentucky. Mike lives in KY’s capitol city of Frankfort with wife Emily and their two boys, Mason and Aaron.
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Over the 2020-2021 period, WDM USA collected friction (continuous, side force coefficient friction using a SCRIM vehicle) and macrotexture (point laser, MPD) on approximately 30,000 miles of state-maintained roads across the Commonwealth of Kentucky. This data yielded several insights into the performance of various aggregates, mix designs, and safety outcomes. In particular, WDM USA used the data to characterize better the role of friction versus macrotexture in contributing to increasing/decreasing crash risk across Kentucky’s network. The relationship was not constant across the network, varying by road classification, geometry, speed, and other factors, underscoring the importance of collecting data at the network level. In this presentation, WDM USA will highlight findings from this data and how that has informed Kentucky’s strategies for designing and maintaining safer roads. By utilizing both short- and long-term strategies to address areas that could benefit from increased friction and texture with varying targets or in varying proportions, Kentucky can optimize network performance and potentially reduce crashes, congestion, and other quality-of-life factors for road users.

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Session 3.4: Enhancements in Safety Assessment by Scott Fritz, ARRB Systems and Jerry Daleiden, ARRB Systems
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Born in Sioux Falls, South Dakota. Retired after a career in US Naval Aviation.
Happily married to a woman from Sweden for 38 years, two children and two grandchildren.
CFME production and technical manuals co-author at the factory head office location in Sweden. CFME installation, operator training and customer service for a global market. Traveled to 58 countries as a production and service team member from 2011-2015. CFME operator for national certification testing and approval requirements on five continents. CFME operator at summer and winter friction workshops in Europe and the USA. CFME operator in Sweden for accident investigation and testing new road standards.
Starting in 2016, Business Development Director for daughter company in USA. The US company has grown to include a service technician and service and parts support with an aim for manufacturing CFME in 2022. ASTM E17 member since 2016.
Abstract:
Agencies continue to seek a greater understanding of the factors that impact safety of the highway infrastructure. Historically, agencies have focused pavement evaluations on ride and cracking of the network. As a result, safety considerations are more of a challenge to accommodate when establishing what treatments might be appropriate. Additional assessments are required to clarify what work might be needed to address segments of concern. Traditional safety assessments have focused on technology that has been sample-based; focused on one factor; and limited testing in critical areas, such as: intersections, ramps and/or sharp curves. Continuous friction measurement now serves to improve evaluations of safety and relationships to the other associated data collected. Equipment is available to measure friction continuously, along with standard surface condition parameters, such as: transverse profile; roadway geometrics; texture; and digital imagery, providing a more comprehensive perspective on the condition of the pavement, and the opportunity to conduct a more thorough analysis of the pavement’s current status and treatment needs (all at traffic speeds). These Comprehensive assessments lead to: 1.) Greater value and applicability of the data collected; 2.) The ability to better optimize network performance and 3.) Improved efficiency and effectiveness of project specific treatment needs. To explore the full merits of these capabilities, this presentation will explore how such tools are being used to support both network and project level investigations. We will also share work being done in the US and abroad to facilitate the standardization of these capabilities. By combining continuous friction measurement with other related traditional surface metrics, the opportunity exists to gain a clearer understanding of the interaction of these critical performance metrics, as well as their impact on the safety of our transportation infrastructure.

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Session 3.5: Texture-Friction Qs and As by All speakers,
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Session 3.6: Session break by ,
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Session 4.0: Moderator by John Senger, IL DOT
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Session 4.1: Developing a Model to Predict Early Friction and Texture Based on Laboratory Observations by Boris Goenaga, North Carolina State University and Shane Underwood, NUSU
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Boris Goenaga is an instructor professor at Universidad del Norte in Barranquilla, Colombia. Boris got a bachelor’s degree and a master’s in civil engineering from the same institution, since his graduation he has been studying pavement performance, transport planning, and highway safety.
In 2018 Boris joined the Transportation Systems and Material research group at the Department of Civil, Construction, and Environmental Engineering of North Carolina State University (NCSU), where he is pursuing a PhD. degree under the direction of Dr. Shane Underwood and Dr. Cassie Castorena.
Boris’ dissertation focused on the relationship of highway safety and road surface characteristics, the integration of a Pavement Friction Management Program (PFMP) and a Pavement Management System (PMS), and the quantification of uncertainty in pavement condition predictions. During his time as student in NCSU he has participated in the TRB as a speaker and was awarded with the International Road Federation (IRF) fellowship in 2020.
Abstract:
There is an increasing realization that pavement surface characteristics play an important role in road safety and consequentially, an increase in the desire to control mix volumetric properties and aggregate characteristics to produce surface layers with adequate friction and texture levels. Although many researchers have developed methodologies to incorporate friction and texture requirements into the mix design process, there is still not a broadly agreed upon formal procedure available. One fundamental element in these works is the development of a model that relates the laboratory protocols that measure friction and texture to the equivalent field values. In this study, 24 field sites were tested using a continuous pavement friction measurement device and a high-speed laser. At nine of these sites, field cores were collected according to standard agency practice and tested in the laboratory. The results were used to develop a set of equations that relate the current laboratory protocols with the new standard to characterize friction and texture in the field. The results are promising and suggest that field cores, such as those that might already be taken for quality assurance purposes, can be used to estimate the friction and texture values in the field.

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Session 4.2: The Pavement Texture Signature: An Alternative Analysis of the Texture Spectrum by Paul Constable, Pathways
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Paul Constable is the Communications Coordinator for Pathway Services Inc. Paul is presenting the work designed and developed by Rudy Blanco and the Pathway Services Research and Development Team.
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A major component of the relationship that exists between vehicle tires and the pavement surface is texture. Studies have shown that both of the primary components of pavement surface texture, microtexture, and macrotexture, are required for establishing a strong correlation to friction measurements. Currently, two data sets and collection methods are used to identify areas within a network where pavement surface texture conditions create friction-related safety concerns. The two-stage process has been employed because: 1) Macrotexture is an indicator of poor surface texture, but it is only one of the two components defining surface texture, and 2) While FN data can be captured using locked wheel skid testing methods, the method is time-consuming and expensive, limiting the use to sampling. Using state DOT-provided core samples for more than 40 different pavement types and conditions, the study resulted in the creation of a digital texture signature for known mix designs in use statewide. The texture signature is a statistical analysis of raw texture data captured by a line laser scanner capable of interpreting changes in pavement surface characteristics where MPD values are equivalent.
Session 4.3: Pavement Friction Management Program Demonstration by Edgar de León Izeppi, Center for Sustainable and Resilient Infrastructure, Virginia Tech Transportation Institute and Ross McCarthy, Gerardo W. Flintsch, Samer Katicha, VTech and Kevin K. McGhee, VADOT
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A pavement friction management program (PFMP) should involve both equipment to collect friction and other relevant data as well as processes to analyze friction and crash data to determine possible friction enhancement treatments on sections that warrant it. This project built on previous experience with PFMPs to (1) propose an enhanced methodology for systematically screening a highway network and identifying sections that may warrant a detailed safety investigation and (2) demonstrate that methodology on the Corridors of Statewide Significance (CoSS) in Virginia. This project evaluated 7,000 miles of highway in the Commonwealth of Virginia. The demonstration collected friction, macrotexture, and geometric data; processed and filtered the data; and conducted a systemic analysis of the network. The analysis investigated the relationship between crashes and friction and other roadway properties and developed Safety Performance Functions (SPFs) to quantify this relationship. The SPFs were then used in empirical Bayes analyses to estimate crash counts before and after friction enhancement treatment and identify sections with friction deficiencies that may benefit from them. The network-level screening identified 1,709 0.1-mile sections of roadway that can benefit from a friction enhancement treatment and thus may require a detailed safety investigation. The application of the selected friction enhancement treatment to the sections could result in a reduction of up 12,949 crashes (approximately 20% of crashes observed over 3 years) in the network analyzed. The friction enhancement treatments would cost about $42 million but could generate potential economic savings over $1.75 billion.
Session 4.4: Texture-Friction Qs and As by All speakers,
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Session 5.0: Moderator by Andy Mergenmeier, FHWA
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Session 5.1: TPF-5(399) Pooled Fund Updates by Andy Mergenmeier, FHWA
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‘Andy Mergenmeier is a Senior Pavement and Materials Engineer with the FHWA. His primary responsibilities include pavement surface characteristics measurement and analysis, construction materials acceptance, and pavement construction. He is the FHWA liaison to the American Association of State Highway and Transportation Officials (AASHTO) Committee on Materials and Pavements Technical Section responsible for management of pavement measurement standards including pavement profiling, friction, rutting and cracking. He is managing the pooled fund study, TPF-5(299)/(399), Improving the Quality of Pavement Surface Distress and Transverse Profile Data Collection and Analysis, which he will be discussing this morning. Andy is the Field Project Engineer for the FHWA Friction Management Program project that includes demonstrating continuous friction measurement technologies within the framework of a friction management program. He came to this position in 2007 after 7 years as the state of Virginia’s Department of Transportation’s (VDOT) State Materials Engineer. At VDOT he was responsible for overseeing preliminary engineering and construction functions, such as, pavement design and construction materials acceptance and testing programs. Before VDOT, Mr. Mergenmeier worked for the FHWA for 15 years in various locations throughout the US.
Mr. Mergenmeier is a Civil Engineering graduate from the University of Kansas, and a Registered Professional Engineer.
Abstract:
TPF-5(399) Pooled Fund Updates

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Session 5.2: Implementing Transverse Pavement Profiler Certification Standards by John B. Ferris, Road Scholar Solutions and Dennis Morian, Jeff Uhlmeyer, and Joe Winkler, QES.
Bio:
Over the past 30 years, Dr. Ferris has established a global reputation for his work on mobile road mapping systems, vehicle dynamics, customer usage and virtual proving ground development, and chassis design for reliability. He earned his B.S. from Carnegie Mellon and his M.S. and Ph.D. from the University of Michigan. He has published over 60 peer reviewed papers since he began this work in 1990, initially for Chrysler, then DaimlerChrysler and ZF Lemförder. In 2005 he joined the Virginia Tech faculty as an Associate Professor in Mechanical Engineering where he has participated in dozens of sponsored research projects totaling over $11 million; his contributions as founder and director of the Vehicle Terrain Performance Laboratory are detailed on the lab website. Road Scholar Solutions offers engineering services to the vehicle and pavement community, drawing from Dr. Ferris’s store of knowledge and experience.
Abstract:
Transportation agencies routinely perform pavement condition surveys to assess the condition/performance of the road surface and determine the maintenance and rehabilitation actions necessary to provide a safe, reliable, and functional roadway. Transportation agencies utilize agency-owned or contracted Transverse Pavement Profilers (TPP) to conduct pavement condition survey data such as rut depth, cross slope, and edge/curb detection. To assist transportation agencies in developing and utilizing quality standards for the collection and analysis of the pavement condition, a national pooled fund study, TPF-5(299), was carried out to develop AASHTO standards for the verification of high-speed transverse profile measurement equipment. Dr. Ferris will present this background information as one presentation. A second subsequent presentation will provide information about two-state certification events held in North Dakota and Texas. Quality Engineering Solutions (QES) was contracted within this pooled fund study to implement and recommend improvements to the five newly developed AASHTO Provisional Standards. This paper presents the implementation of these Standards for the two separate certification events performed for the North Dakota Department of Transportation (NDDOT) in Bismarck, ND, and the Texas Department of Transportation (TxDOT) in College Station, TX. Both events resulted in many lessons learned in the practicality of conducting the certification process for each standard, including potential modifications to the AASHTO Provisional Standards. The results from each event are presented, including example results and analysis from the TPP’s evaluated.
Session 5.3: Piloting Certification and Verification Methods for Transverse Pavement Profiles by Amanda L. Gilliland, Transtec Group and Abbasali TaghaviGhalesari , George K. Chang
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Amanda Gilliland is a pavement engineer and project manager at The Transtec Group, a pavement design and research consulting firm. Ms. Gilliland’s expertise is related to pavement surface characteristic data quality collected by traffic-speed vehicles. She is actively working on the research team for the Guidance for Quality Management of Pavement Surface Condition Data Collection and Analysis project. Under this project, she has piloted recently developed certification and verification procedures for transverse pavement profiling equipment. Ms. Gilliland is proficient in ProVAL pavement profile software. She provides help desk support and assists with training workshops through the ProVAL support center.
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Modern-day pavement surface condition (PSC) data collection technology has advanced significantly in the past decade. Current high-speed PSC data collection vehicles are often equipped with multiple subsystems that simultaneously collect geospatial location information, surface elevation data, and high-quality video and imagery that can be used to extract pavement profiles and calculate surface distress data. Most US DOTs use the collected PSC data to determine the condition of roadway networks and make decisions related to pavement budgets, maintenance programs, and rehabilitation strategies. Since roadway networks are the most significant asset for DOTs, major funding is spent maintaining them. Therefore, ensuring quality PSC data is used to support decision-making is essential. All DOTs are required to submit Data Quality Management Plan (DQMP) to FHWA to detail their PSC data quality assurance (QA) practices. Under the FHWA DQMP project, all DOT’s DQMPs were reviewed, and common issues and successful practices were identified. Most DOTs have struggled to implement certification and verification procedures appropriate for the new complex technologies based on these reviews. Six AASHTO standards for high-speed transverse pavement profile (TPP) system certification and verification were published in 2021. Because these standards aim to resolve the most common DOT PSC QA challenges, the standards were piloted with three participating DOTs under the FHWA DQMP project. This presentation shares the results and experiences of piloting these AASHTO standards at the DOTs. The pilot project results provide valuable lessons learned that will be helpful to other DOTs and agencies interested in implementing the certification standards in their data quality programs.
Session 5.4: Transverse Profile Qs and As by Andy Mergenmeier, FHWA
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Session 5.5: Session break by ,
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Session 6.0: Moderator by Steve Hale, NVDOT
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Session 6.1: City of Austin Case Study: Practical Application for Utilizing International Roughness Index for Municipalities by Reuben Williams, Applied Research Associates and Veena Prabhakar, City of Austin
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Mr. Reuben Williams received both his bachelor of science and master of science in civil engineering degrees in from the University of Texas at El Paso. Mr. Williams has worked as a pavement engineering consultant since 2004, providing pavement engineering, design, and management services to agencies throughout Texas as well as many agencies across the United States for over 18 years. Mr. Williams has served on the AFD80 committee on the Strength and Deformation Characteristics of Pavement Sections for the Transportation Research Board and presented at numerous national and regional conferences on pavement design, structural evaluation, and pavement management. Mr. Williams has managed over 100 pavement management and engineering projects for cities, counties, and other entities as well as state-wide pavement data assessment projects accounting for over 100,000 miles of roadways.
Veena Prabhakar, PE, serves as the Pavement Management Engineer at the City of Austin. Ms. Prabhakar has over 22 years of experience in the field of pavement engineering. Ms. Prabhakar has worked on a wide range of pavement engineering projects in the areas of research, consulting and public agency work. Her areas of experience include pavement design, pavement structural evaluations, pavement condition evaluations and pavement management work. She has been with the City of Austin for 10 years.
Abstract:
Pavement management decisions are driven based on pavement condition data, including roughness and distress assessments. The International Roughness Index (IRI) collects and utilizes roadway roughness data to measure the road roughness for municipalities that present different challenges than those experienced on high-speed roadways maintained by state DOTs. In addition to lower data collection speeds, urban areas exhibit less free-flowing traffic, traffic calming devices, rough transitions across intersections, and other localized features (utility castings, railroad crossings, drainage features, etc.) that influence the collected IRI values and do not accurately reflect the actual roughness of the roadway that can be corrected through pavement maintenance, rehabilitation, or reconstruction. Texas, the City of Austin, relies heavily on representative roughness scores (IRI) to make pavement maintenance treatments and prioritize roadways for maintenance. As such, the City has taken the approach of performing several filtering operations on collected IRI values. The results represent the existing pavement surface and are conducive to making reliable pavement management decisions. This presentation will discuss the City’s methodology to incorporate IRI in their decision process the methods used to filter IRI information. The results are usable in the City’s decision process, and considerations and limitations of utilizing IRI in a municipal environment still need to be considered.

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Session 6.2: Illinois Roadway Construction Profile Incentive/Incentive Program by John Senger, ILDOT and Robert Rescot, ILDOT, Vince Belitsos, ILDOT
Bio:
John Senger received a Bachelor of Science in civil engineering from Bradley University. John has been at the Illinois Department of Transportation for 6 years and is currently the Engineer of Pavement Technology. Prior to joining IDOT, John worked as a consultant engineer where he was involved in a wide variety of projects.
Robert Rescot, Ph.D., P.E. is a senior civil engineer with Applied Research Associates in Springfield, Illinois. Robert is a registered professional engineer. He earned both a bachelor’s and master’s degree in civil/transportation engineering from the University of Missouri, and a Ph.D. in civil/transportation engineering from the University of Kansas. Working closely with the Illinois DOT, he led early research efforts to bring Illinois into the IRI/MRI family and has continued working through the entire specification development and implementation process. He also reviews and organizes the friction testing program for the state of Illinois. When not thinking about roads or data, he enjoys referring ice hockey, being a Cub Scout leader, and tinkering in his garage workshop.
Vince Belitsos, P.E. is a senior civil engineer with Applied Research Associates in Springfield, Illinois. Vince is a registered professional engineer in both Illinois and Florida. He earned his bachelor’s degree in civil engineering from the University of Michigan. Working alongside John Senger and Robert Rescot, Vince has assisted in the creation and management of the Illinois DOT IRI specification program. Outside of work efforts, Vince enjoys biking, hiking, and spending time with his family.
Abstract:
In 2021 the Illinois Department of Transportation rolled out the agency’s first specification for profile testing of qualifying new pavement overlays and new pavement construction based on International Roughness Index (IRI) values. Before this implementation, the agency used profile index for a similar purpose but covered a narrow scope of projects. Following extensive research on historic Illinois construction data, industry consultation, and evaluation of other agencies’ practices, a hybrid percent improvement specification was approved for 2021. This new IRI-based specification expands the scope of profile testing to cover a broader scope of projects, including some urban low-speed pavement overlays. It also included a quality assurance component based on statistical methods. Results summarizing the nearly 70 contracts completed using the new specification and feedback from an industry workshop will be shared in the presentation.

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Session 6.3: An Agency Roadmap to Incorporating Intelligent Construction Technology to Improve Smoothness and Longevity – A Contractor’s Perspective by David Ford, Pavement Recycling
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My name is David Ford. I graduated from California State University Long Beach with a degree in Civil Engineering. I have worked for Pavement Recycling Systems for over three years with a focus on Intelligent Paving products including the inertial profiler.
Abstract:
Agencies want to incorporate BIM for Roadways/Intelligent Construction Technologies to improve the smoothness quality and longevity of roadways and for better administration of projects. Contractors and manufacturers want to make money. Are these three goals mutually exclusive? A contractor, who was an early adopter, provides insight into the answer to this question and discusses why they invested in Intelligent Construction Technologies (IC). A unique perspective will be provided from someone who subcontracts for over 400 different general contractors a year on over 5,000 projects. In addition, a behind the scenes look will be taken into a contractor’s psyche and the ways different contractors are motivated in their business strategies such as high revenue and low margin vs selective and high margin, value added vs commodity based, and cheap and easy vs challenging and high quality. Finally, a suggested roadmap will be outlined that will assist Agencies in successfully navigating these motivations so as to implement IC into specifications in the most efficient and painless way possible to ensure the highest quality is constructed on their roadways.
Session 6.4: Profiling Qs and As by All speakers,
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Session 6.5: RPUG business meeting by Scott Madison, Pathways (RPUG president)
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Session 6.6: lunch by ,
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Session 7.0: Moderator by Colin McClenahen, PennDOT
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Session 7.1: Crack Detection from High Quality Surface Images by David Malmgren-Hansen, Greenwood Engineering
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Greenwood Engineering’s Surface Imaging System (SIS) provides high-quality continuous surface images independent of sunlight conditions, of typ. 4m width, measuring at high speeds (e.g., 110km/h), with millimeter pixel resolution. This offers the potential to measure a large number of lane kilometers per day, far more than what is feasible to analyze manually, e.g., cracks. As Deep Neural Networks, a family of mathematical algorithms, have shown many innovations in Computer Vision, these are the obvious choice for automizing the tedious task of analyzing surface images. Greenwood Engineering has developed a Deep Neural Network for automatic crack detection in SIS images with state-of-the-art performance, classifying cracks, patches, potholes, open-joint, and edge-cracks. The algorithm, trained on more than 1 million images, achieves an accuracy of >94% on an out-of-sample test set of 150,000 images with a balanced number of samples between damaged and non-damaged surfaces. During development, challenges arose, such as class imbalance, strategies for handling these, and the sizeable computational challenge are presented in this work.
Session 7.2: Deep Learning Based Object Detection Methods for Pavement Distress Detection: An Experimental Study Using Rider’s View Image by Haitao Gong, Texas State University and Jueqiang Tao, Xiaohua Luo, and Feng Wang, TX State
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Mr. Haitao Gong is currently a doctoral student in Materials Science, Engineering, and Commercialization program at Texas State University. He has a bachelor and master’s degree in Transportation Engineering, and a minor in Computer Science. Before attending Texas State University, he worked as a transportation engineer and infrastructure investment consultant in China Communication Construction Company for 5 years. Currently he is working on his doctoral dissertation, which is focused on “Improving pavement distress measurement using deep learning methods”. His research work has produced two publications, and he is working on commercializing a related business idea with the fundings from NSF I-Corps program. Beside his research interest in AI, he is also an augmented reality enthusiast, a part-time entrepreneur, and an outdoor lover.
Abstract:
Pavement distress measurement is an essential component of automated pavement condition surveys. Recently, Deep Learning methods have been intensively studied in distress classification, detection, and segmentation. Training data and network architecture need to be precisely and rigorously addressed to build an effective Deep Learning-based distress measurement model. This presentation will introduce an experimental study of how existing deep learning-based object detection methods perform pavement distress detection using rider’s view images. Both pavement image data and network architecture will be analyzed to explore the major factors that affect the performance of deep learning models. First, characteristics of pavement image data will be discussed, including data size, image type, and annotation. An open-source dataset with 7,237 pavement images was adopted for this experimental study. Second, state-of-the-art object detection methods will be introduced, including widely applied YOLO and Faster R-CNN. Three of the promising methods were adopted for model development and performance evaluation. Third, the results of a series of experiments will be presented and discussed. These experiments were conducted with different network architectures and data manipulation methods. According to the experiment results, the existing state-of-the-art object detection methods can pavement distress detection. However, more customized deep learning architectures for pavement cracks are expected for refined detection in the future. Data size and annotation quality are two other significant factors significantly affecting detection performance. It is suggested that datasets with high quality and quantity are needed to facilitate the research in pavement distress detection in the future.

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Session 7.3: Comparable and Implementable Cracking Definitions in the Era of 3D Data and Automation by Kelvin Wang, Oklahoma State University and WayLink Systems Corporation

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This presentation details new cracking definitions proposed in the NCHRP Project 01-57A completed in 2020 by the OSU team. Multiple methods and software exist for defining, classifying, and reporting cracking data. The methods and the cracking data they produce are not always comparable between states, even if similar data collection and detection technologies are used. One outcome of this situation is that vendors must customize the cracking definitions for each client they serve. Standardizing pavement cracking definitions is needed to unify data reporting, sharing, and evaluation. This need is becoming more urgent as 3D data collection and AI-based processing has matured. The presentation reveals the newly proposed three cracking definitions to define cracking measurement terms for uniformity and potential standardization, building upon work done in AASHTO PP 67 and 68. The standard definitions will aid in sharing information among agencies and vendors, reporting to FHWA, and setting national, state, and local performance goals. The new definitions are tailored for automation and meeting project-level, and network-level pavement engineering needs in ME design, management, and reporting.

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Session 7.4: Introduction of the New ASTM Standard E3303, “Practice for Generating Pavement Surface Cracking Indices from Digital Images” by Michael Nieminen, ICC and Danilo Balzarini, ICC, and Jerry Daleiden, ARRB Systems
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Many have become reasonably familiar with the challenges of transitioning from manual distress rating to automated distress rating. Traditional distress protocols have taken only limited advantage of the capabilities of new automated systems. Efforts have been made at the State and Federal levels to develop more conducive protocols for automated technology. Unfortunately, many local agencies still closely follow ASTM Standards, where minor (if any) refinements have been made in decades. A standard was developed to make pavement assessment more repeatable by generating cracking indices and other required parameters objectively and quantitatively to bridge this gap. The cracking indices proposed, the Pavement Surface Condition Metric (PSCM) and the Pavement Surface Condition Index (PSCI), are unitless and calculated straight from total length width and area measurements to eliminate the subjectivity associated with the human rating of the pavement distresses. By sharing the approach of this new standard, it is hoped that further opportunities for synergy can be identified and achieved between the State and Local level protocols. We will demonstrate how to apply the new standard and its advantages compared to other distress protocols. The presentation will show a practical application of determining the Pavement Surface Condition Metric (PSCM) and the Pavement Surface Condition Index (PSCI) and demonstrate the new metrics’ repeatability.
Session 7.5: Distress Qs and As by All speakers,
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Session 7.6: Session break by ,
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Session 8.0: Moderator by John Andrews, c LLC
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Session 8.1: Road Health – Using connected vehicles to objectively monitor an entire road network by Björn Zachrisson, NIRA Dynamics AB
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Strategist within the infrastructure area of NIRA Dynamics AB. Been in the software industry since graduation 2006, with a master’s in computer science. Worked in the mobile communication and aviation industry prior of joining NIRA. Before the current position, Björn was responsible for NIRA’s in-vehicle products towards Volkswagen. In the NIRA connected portfolio Björn started out coding the foundation of what is today NIRA’s backend, where data from millions of vehicles are ingested and presented as road information layer.
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Since 2014 NIRA has been working with connected vehicles, bringing over 20 years of experience in embedded vehicle software products into today’s connected world. The NIRA software is installed at the production facilities of the VW Group passenger vehicles. Each vehicle (~2 million annually) acts as a probe gathering. This makes it possible to continuously monitor the road surface conditions in an entire road network. NIRA creates virtual sensors through sensor fusion of multiple signals using software only. The output is data such as road state (roughness in terms of IRI), potholes & driving anomalies is being collected and updated on at least a daily interval. There are many challenges in the field of vehicle data. One of them is to challenge the legacy methods and standardizations of the equipment used; the focus has been on improving the technology but never really looking into the added value from the added precision and increased coverage. Compared to a laser scanner, the output from vehicles will always be different, but does that matter if it is consistent? The technology rolled out across Europe, and the US has been tested in production in the Netherlands and Scotland. Combining data from many vehicles gives objective, up-to-date information of the roads at a whole network level. This enables the possibility of tracking road wear trends and planning for reactions to sudden road damage. NIRA is also researching how well the “slippery when wet phenomena can be detected by vehicles. Initial tests in the UK (Mira) and France (Nantes) show great promise for the technology.
Session 8.2: Network-Wide Pavement Historical Comparison by Jacob Pellmann, Mandli Communications
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Jacob Pellmann graduated from University of Wisconsin-Madison with a degree in civil engineering. During his 9 years at Mandli communications Jacob has aided in providing pavement condition data to numerous state and local agencies. In his current role as Pavement Product Manager Jacob oversees all of Mandli’s pavement offerings and finds new solutions for the pavement needs of DOTs
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Historical roadway data comparisons provide additional context to assist with decision-making around pavement management. For many agencies, one major challenge is ensuring data is accurately being compared. In this presentation, Mandli Communications will address how we compare historical data to ensure an accurate comparison and why this is beneficial for pavement management. Working with the Tennessee Department of Transportation (DOT) over the years, we’ve modified our deliverables to ensure the DOT quickly ingests datasets in the most meaningful way. In some instances, this has meant assigning slightly different mileages to data collected simultaneously to ensure it fits with the end-users other data. For example, we have experience formatting our datasets for different departments within Tennessee DOT. In the past, this had included when one department gave us the starting and ending mileage that they expected, and we adjusted our mileages to fit appropriately within that range. In this presentation, Mandli Communications and Tennessee DOT will share why their collaboration ensures an accurate historical comparison is beneficial for other agencies. We will address how this historical comparison provides more meaningful data than other methods and how this is a cost-benefit for the agency over time.
Session 8.3: Road Scanning Innovation based on Autonomous Vehicle sensor by Jacopo Alaimo, Xeno Matrix
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Jacopo joined XenomatiX to lead the expansion of its revolutionary and simple LiDAR technology in the North American region. With the vision to contribute to safer roads and transportations he is convinced of the advantages that the True Solid State could bring to these sectors. In his earlier career he covered R&D managerial positions in Marelli and Koito. Jacopo earned an MS in Aerospace Engineering and a Mountain Guide Certification, qualifications that reflect his passion for exploration and techniques.
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Road scanning tools have significantly improved in the last 20 years by introducing advanced technologies like Lasers, Lidars, Accelerometers, and Stereo-Vision. These instruments allowed to speed up the measuring campaigns and to move from qualitative to quantitative evaluation. Nevertheless, road mapping costs are still high, limiting the updates to larger organizations and making these technologies inaccessible to small companies and municipalities. The latest development in autonomous driving (AD) could bring a significant revolution in connection with the developments of the vehicle to infrastructure communication (V2I). This presentation will introduce these concepts showing state-of-the-art sensors for AD and how this revolution is already making advanced mapping tools accessible to everyone.
Session 8.4: Using automated data collection methods for municipal governments in the Chicagoland area by Jake Walter, Applied Research Associates
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Mr. Walter has spent 24 years implementing infrastructure management systems across North America with a specialization in pavement management. He has worked in all aspects of these projects including data collection, evaluation, implementation, quality control, and project management. His specialty is the implementation and use of software tools for asset management and the supporting data required by those systems. Mr. Walter has a degree in Civil Engineering from The George Washington University and a Professional Engineer’s license from the State of Ohio.
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Pavement management systems (PMS) enable agencies to optimize their maintenance and rehabilitation (M&R) budgets to extend the life of the pavement and make best use of taxpayer funds. Recent developments in data collection technologies include automated data collection and distress identification which is the basis of the analysis performed by the PMS. While this technology is widely used at the state level, its use at the municipal level is less common. However, the use of automated pavement evaluation may reduce the overall implementation cost while producing reliable and repeatable data. The Chicago Metropolitan Agency for Planning (CMAP) implemented a program in 2018 to develop PMS systems for municipalities in the Chicagoland area. As part of this program, ARA implemented PMS systems for 22 agencies using Paver as the software tool. This study discusses the issues and results of the PMS implementations using automated data techniques. Pavement data was collected using both Dynatest MFV and ICC IrisPro vehicles. Both vehicle type are equipped with the Pavemetrics Laser Crack Measurement System (LCMS), right-of-way camera(s), and an inertial profiler. This data was fed into the ICC Connect data management and processing software to generate a distress type, quantity, and severity database. ARA used the data created by these existing tools and created a custom tool to upload the automated data to Paver and deploy the system. In addition to the pavement evaluation, ARA collected information on inventory and pavement repair practices to develop custom pavement performance models and treatment selection criteria for each municipality. The results of this work were a set of plans that recommended where to perform work while focusing on pavement preservation. The PMS implementation process using automated data collection methods proved to be efficient and provided the municipalities with an objective, repeatable method to manage their pavement networks.
Session 8.5: Pavement Condition Surveys Qs and As by All speakers,
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Session 8.6: Closing and Introduction to Next RPUG by Scott Mathison, Pathways (RPUG president)
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Session 9.1: TPF-5(354) Pooled Fund Meeting by Dave Huft, SDDOT
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Improving the Quality of Highway Profile Measurement – Pooled Fund Meeting

This meeting is for its members and all others interested in this subject.

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Session 9.2: TPF-5(345/463) Friction Pooled Fund Meeting by Kevin McGhee, VDOT
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Pavement Surface Properties Consortium: Phase III – Managing the Pavement Properties for Improved Safety – Pooled Fund Meeting

This meeting is for its members and all others interested in this subject.

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Session 9.3: ProVAL workshop by George K. Chang, Transtec Group and Steve Karamihas of UMTRI, Amanda Gilliland and Abbas aghaviGhalesari, Transtec Group
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This workshop is hands-on centric with ProVAL software excises. It will cover some fundamental ProVAL viewing/analysis (data import wizard, Ride Quality Module, work-around to trick stationing), then get into more in-depth topics (Profile Editor/Filtering, PSD module, SAM/grinding simulation, profile comparison/PCM module). All participants need to bring their laptop computer preinstalled with ProVAL 3.61.