2017 Speakers Bios

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Through a regional pooled fund, this program of research focuses on optimizing pavement surface texture characteristics. Phase II of the program continues to support the member’s effort to produce high-quality surface properties measurements but focuses on supporting the enhancing and adoption of emerging friction and macrotexture measurement technologies and the integration of these measurements into the next generation of pavement asset management systems. The program includes the following main broad activities: (1) equipment rodeos, (2) technology transfer, and (3) research on emerging topics. This presentation will provide an update on recent activities, as well as an overview of existing and expanding resources.

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The Highway Performance Monitoring System (HPMS) pavement data item reporting requirements have been modified in response to the MAP-21 and FAST Act legislation. An overview and detailed description the new HPMS requirements will be given in support of the FHWA performance monitoring and management effort as they pertain to the final associated rulemaking effort. Data items primarily include IRI, rutting, faulting, and cracking along with various associated HPMS item requirements.

While most States collect these data to some degree in their own systems, there are some new requirements in the data to be reported in the HPMS in support of high quality and nationally consistent performance measures. These will be presented with the HPMS-specific logistics of reporting these data.

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Pavement roughness, in terms of the International Roughens Index (IRI), has been required by the Highway Performance Monitoring System (HPMS) since 1988, and it is now required by the MAP-21 and FAST ACT legislation. This presentation will review the IRI data submitted by the State Departments of Transportation (DOTs) to the HPMS over the period of 2013 to 2015. The IRI is considered not only one of the more reliably measured values within the HPMS, but also one where completeness has been observed as fairly stable over time, with approximately 47,000 miles of data provided in each of the referenced years. Accordingly, these data provided an opportunity to review the time-series trend in the HPMS IRI data.
The average IRI has remained fairly constant for the three years in question, with the overall averages of 81 in/mile, 74 in/mile, and 79 in/mile for each of 2013, 2014, and 2015, respectively. These average values do not provide much indication of how the data truly change over time. A better picture is provided by distributions of the values, which illustrate that the data are fairly stable. Moreover, selected IRI data were reviewed to identify specific changes at a location and these also demonstrated the stability of the measure with an average increase in IRI of 0.85 in/mile from 2013 to 2014 and an average drop of 1.49 in/mile from 2014 to 2015.
The IRI data in question were reviewed to identify the various factors that impact the values. Statistical analyses demonstrated that the IRI distributions were different for different types of terrain (level, rolling, mountainous), different climate zone, and different levels of population (urban vs rural). These analyses also suggested that the data collection conditions should be considered when trying to compare data from one area to another.

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Knowing the condition of the pavement surface can help a road agency manage its road network from both a performance and safety perspective. One such parameter is macro-texture, which is a property of the pavement surface that can affect skid resistance and also plays an important role in dispersing water in the wheel paths, thus reducing the potential for hydro-planning.
Automated 3-dimensional (3D) data collection systems, which have primarily been used for the detection of pavement defects and transverse profile measurement, also have the ability to measure other pavement condition parameters and features. One such parameter is pavement macro-texture.
A validation exercise was undertaken by the Australian Road Research Board to assess how well the outputs from the 3D system matched those from a ground truth texture measuring device over a series of test sites. This paper presents the results of the validation and provides additional information into how this 3D technology is able to measure pavement macro-texture.

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This presentation will focus on distress data classification collection and evaluation using International Roughness Index (IRI), Surface Distress Index (SDI) and other overall Pavement Condition Indices (PCI). It will identify a few technical issues with measuring and rating pavement conditions, including individual distresses and performance indices commonly used in pavement management. Then presentation will then explore a number of fundamental problems relating to pavement evaluation and maintenance management, ranging from pavement condition data collection to pavement maintenance investment program. Finally, the presentation discusses accountability of pavement condition rating methods and comparability of the current pavement condition evaluation results, which lead to the needs for developing a universal conversion that can compare different pavement condition rating systems to a standardized ranking system. An example of using different road condition rating methods to demonstrate the issues and needs for a standard rating system, including engineering specification and validation of pavement performance indices. To ensure quality of the automated pavement condition assessment, a series of comparative measurements in the field were conducted to verify whether each of the stages has been accurately processed, such as pavement distress detection, classification, condition evaluation and reporting.

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This presentation would address the development of a comprehensive hydroplaning risk assessment tool that can be used by transportation agencies to help reduce the potential of hydroplaning. The tool treats hydroplaning as a multidisciplinary and multi-scale problem, integrating the most appropriate surface water drainage, tire, vehicle, and fluid dynamic models to predict the hydroplaning risk for new and existing roads.

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Traditional structural testing is slow in speed (2km/hour) and consequently most pavements are not tested. Pavements in cities represent 30% of a total of 24 million kilometers globally, with structural tests being rarely carried out. A compact and reliable Rolling Weight Deflectometer which would be able to test at 80 km/h and that at the same time is easy to maneuver in cities without creating congestions nor safety concerns would change this situation.
The Rapid Pavement Tester, RPT, is the new Dynatest product, now under its last development phase, that will start offering high speed deflection measurements in 2018. The RPT integrates the RWD into a new platform to collect simultaneously structural and functional data.
Using image recognition and a normalized cross-correlation algorithm, the RPT identifies very accurately the same spot on the road scanned by subsequent lasers. This allows the effects of the beam translation and rotation as well as the texture of the pavement to be removed.
The Dynatest RWD is based on a relatively short beam without any significant bending and the data will need to be averaged over less distance which will result in shorter distance between data points. The short beam also allows for a short truck that is easy to maneuver in a city landscape. The performance of the Raptor has been tested towards repeatability and load scalability with success, and the its ability to reproduce the true pavement response has been verified by in-pavement sensors. Shadow testing using the FWD and the Raptor is ongoing.

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Mr. Mathison earned both his MBA and undergraduate degrees from the University of Oklahoma. He has since held many positions in information technology and has more than a 15 years of pavement management experience. He has also overseen the deployment and management of more than a petabyte of condition data nationwide.

He’s been married for 15 years, has three kids and is the biggest home automation nerd in the room.

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3D automated transverse profile data provides our industry with surface characteristics data that we could only dream of just a few years ago. With network-level collections producing TBs of data annually, much of this data is automated not just for collection, but for processing, QC, QA and reporting. Real-world environmental characteristics, such as narrow lanes, areas of shoving, erosion, curb/gutter, missing/failing lane markings can lead to difficulties for automated techniques to effectively QC and accurately report rutting data without human intervention or specifically-developed automated QA modules. This presentation will detail several techniques and methodologies utilized in statewide data collection projects to mitigate the effects of difficult, real-world scenarios for automated data collection and reporting.

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For decades, fully automated cracking surveys for highway and airfield pavements were not possible despite of enormous advances in hardware and software development on worldwide basis. Currently substantial amount of resources must be dedicated to manually editing results from the “automated’ processing to achieve needed precision and bias. The challenge in automation is to maintain acceptable levels of precision and bias at all times disregard pavement types and their surface conditions, and without substantial manual intervention. This presentation discusses the challenges and frustrations facing the industry for decades in obtaining pavement cracking information and their subsequent applications in both design and management activities. Progress in the recent decade from industry, users, and researchers is summarized. The presentation also illustrates the motivation in 2015 and early 2016 for the research team to change direction and embark on developing a completely different technical approach to automated survey of pavement cracking. The Deep-Learning (DL) methodology is based on decades old computerized concept of Artificial Neural Network (ANN) with substantial improvements to the network structure and vast increase of the network sizes, resulting in many recent successful applications with super-human capabilities. The presentation also discusses the recent trends in cracking and data standards including the FHWA led Pooled-Fund TPF 5(299) and various NCHRP and AASHTO efforts. The efforts from different agencies at national and state levels for pavement design and management all point to a consensus that fully automated cracking survey is needed and relevant standards should be made or modified to accommodate the automation. Based on the need of true automated pavement cracking survey and the potential capabilities of DL, since early 2016 a research team was assembled with over 10 staff and students in the US and China to start the DL work, and in the fall 2017 the team completed the initial development of a fully automated cracking survey solution. This presentation focuses on the background and system design of a DL based neural network targeting at true full automation.

For the past 18 years, Mr. Frith has been applying the modern technical developments in pavement management, maintenance, and design in the consulting field. He currently manages the Independent Validation and Verification efforts for over 22,000 miles of pavement management data for the Virginia DOT, and manages the daily Quality Assurance ratings of 19,000 miles of pavement for the North Carolina DOT.

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A presentation of the independent distress verification and validation (IV&V) effort conducted for the Virginia Department of Transportation in 2016 and 2017 will consist of discussing the details of the data quality management plan being used, how and why some non-conforming data sets passed the IV&V checks when they should not have, and how this issue was remedied in 2017.

In 2016, larger than normal data deliveries were provided for the secondary route pavements, resulting in nearly 3 times the number of QA samples typically collected. Although the initial deliverable passed all of the QA checks, it was later determined that portions of the data set did not include all of the longitudinal and transverse cracking that should have been reported. A detailed investigation ensued which resulted in changes to the existing IV&V process, which was then applied to the 2017 data.

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Road roughness data e.g. IRI is useful for the pavement maintenance management. But in fact, many road administrator faced difficulties how to use roughness data. Because of it spend much cost previously, so, they make decision without data. Recently, a convenient and low cost measurement method e.g. smartphone type was developed. It is removing one the obstacle of cost side. On the other hand, the operational difficulties are still remaining.
In Japan, at Aizu-Wakamatu city, from before, the local government road administrators are doing regular patrol few times a week for human visual inspections and simple repairs. From January 2015, they are starting regular roughness measurement by using smartphone. It is measuring on this patrol, so it is not increasing man-hour cost. Previously, patrol result is hand writing on the notebook. From June 2016, they are starting to record site photo with GPS data and one word comment by using smartphone. As the result, there are roughness data and repair results.
In this presentation, it is using both recording, relationship between repair situation and roughness conditions is discussed.

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The final rules for reporting pavement and bridge performance measures were published by the Federal Highway Administration (FHWA) in January 2017 and these rules became effective under the Fixing America’s Surface Transportation (FAST) Act in May 2017. While most highway agencies are in the process of determining how to meet these reporting requirements, the ultimate objective of performance management might be overlooked. Every highway agency has their own performance measures, which they use for network-level decision making to assist in generating the most cost-effective program of improvement projects. There is a gap between performance measures used for reporting existing condition versus the performance measures used for management decisions. This presentation is trying to address some of the challenging questions in closing this gap.
Should the highway agencies change their performance evaluation methodology and protocols to match the federal rules, or should they establish a correlation between their performance indices and the federal reporting measures? There is a need to investigate how agency investment decisions are reflected in the performance measures reported to the FHWA. With the additional data available from higher resolution equipment, are we focused on information that is actually used in decision making, or are we losing the signal among the noise? How can the reporting requirements help advance the state of the practice in performance-based decision making? What limitations should agencies be cognizant of to avoid confusion?

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