National Academies Press: OpenBook

Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports (2019)

Chapter: Chapter 8 - Next Generation Pavement Condition Data

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Suggested Citation:"Chapter 8 - Next Generation Pavement Condition Data." National Academies of Sciences, Engineering, and Medicine. 2019. Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Washington, DC: The National Academies Press. doi: 10.17226/25566.
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Suggested Citation:"Chapter 8 - Next Generation Pavement Condition Data." National Academies of Sciences, Engineering, and Medicine. 2019. Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Washington, DC: The National Academies Press. doi: 10.17226/25566.
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Suggested Citation:"Chapter 8 - Next Generation Pavement Condition Data." National Academies of Sciences, Engineering, and Medicine. 2019. Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Washington, DC: The National Academies Press. doi: 10.17226/25566.
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Page 95
Suggested Citation:"Chapter 8 - Next Generation Pavement Condition Data." National Academies of Sciences, Engineering, and Medicine. 2019. Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Washington, DC: The National Academies Press. doi: 10.17226/25566.
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Page 96
Suggested Citation:"Chapter 8 - Next Generation Pavement Condition Data." National Academies of Sciences, Engineering, and Medicine. 2019. Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Washington, DC: The National Academies Press. doi: 10.17226/25566.
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Page 96

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92 C H A P T E R 8 For decades existing pavement condition data standards and practices, such as using the ASTM PCI procedure to identify pavement distresses, have aligned well with what is referred to as manual data collection. However, the technologies currently being applied to pavement condition data collection and analysis are expanding at a rapid pace. Where in the past a visual condition survey was the standard and the results from either a project- or network-level survey informed decisions on everything from pavement maintenance and rehabilitation to budget and staffing needs, it is clear that advancing technologies have already brought about significant changes in practice and will continue to do so in the near future. It is challenging to identify which pavement condition data technologies or procedures should be adopted by an airport. Table 16 provides suggestions for data storage, while Figures 36 through 43 offer guidance on data collection procedures based on the user or intended use. Looking toward the future, it is suggested that any change in data collection technology from current practices should provide a tangible advantage, such as one or more of the following: • Improved compliance, • Faster data collection and processing, • Lower costs, • Greater accuracy, and • Improved access to data. This concluding chapter looks to the future of airport pavement data collection and the ability of existing or emerging technologies to provide these benefits. Improved Compliance As discussed in Chapter 2, there are a number of FAA-dictated requirements for Part 139 and federally obligated airports in the U.S. related to pavement data collection. Broadly these relate to daily, continuous, periodic, and either annual or triennial inspections to identify either safety or pavement performance defects. The most promising technology to improve compliance with FAA requirements for self- inspection is the use of sUASs/UAVs. Notwithstanding regulatory issues, which are both in a state of flux and create barriers to use, this technology can be used to rapidly collect a high-resolution, continuous image of pavements that otherwise would require one to two staff per vehicle to collect. The size of the airport impacts how much of a benefit could be provided by using an sUAS for inspections, but additional benefits include the creation of a permanent or semi- permanent digital record that could be reviewed if needed and the potential to use computer vision (the science and technology of machines that “see”) to interpret the captured images. Next Generation Pavement Condition Data

Next Generation Pavement Condition Data 93 Variations of this technology have already been deployed at airports such as Hartfield-Jackson Atlanta International Airport, Front Range Airport, and a GA airport in Maine for pavement inspection and at other airports for other inspection purposes (Wysocky 2018). A recent survey of state highway department practices by the American Association of State Highway and Transportation Officials (AASHTO) reported that 35 are “using aerial drones regularly or are testing the devices for possible use” and six are already using drones for pavement inspections (Dorsey 2018). Faster Data Collection and Processing The ability to collect pavement condition data more rapidly is of value for at least two reasons. Existing data collection methods require physical access to pavements. At busy air- ports without excess capacity, this access is often only granted at night when operations slow down and sometimes in slices of time as small as 4 or 5 hours. In the case of single-runway airports operating near capacity, regular access comes in an even narrower window or is granted with a negative impact on operations. For access-constrained airports, any pavement data collection method that functions more rapidly and has less of an impact on operations is highly desirable. In this regard, both automated distress surveys (e.g., 3D, LiDAR, sUAS) and faster struc- tural evaluations have the potential to provide this greater value. However, at the same time it is acknowledged that these data collection methods may not be applicable in some cir- cumstances. In particular, on aprons near terminal buildings and around parked aircraft or ground handling equipment, a manual survey may be the only way to collect pavement condition data. Similarly, the continuous measurement of deflection or the fast FWDs also will generate required data more rapidly. As noted previously, these particular technologies are not yet deployed to evaluate airfield pavements. The ability to process data and provide results more rapidly is also an advancement that is on the near horizon. Pavement condition data generated in digital format should ultimately be interpreted by software developed for this purpose, and in several instances, this describes current practice. For example, many users of 2D or 3D imaging use customized software to identify and report recorded distresses. Where it is solely the software that identifies distresses, the process is referred to as “automated,” but if human assistance is used then the process is referred to as “semi-automated.” The less involvement by humans that is required to extract pavement condition data (without sacrificing quality), the more rapid the reporting process can be completed. Shortening the data processing and reporting steps puts useful data in the hands of decision makers sooner, potentially enabling more timely decisions regarding maintenance, rehabilitation, and planning for capital projects. Lower Costs Ideally there will be cost savings that are part of the deployment of new pavement data collection and storage technologies. The costs of any phase of the pavement data process were not considered in this study so it is not appropriate to try to compare data collec- tion and processing costs here. However, with the different technologies available or soon to be available for pavement data collection analysis, there will undoubtedly be differences in costs. An agency will need to weigh the cost of data collection against the value of the obtained data.

94 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Greater Accuracy Advancements in technology do not automatically mean greater accuracy, although perhaps in many cases that is an implied benefit. Table 18 identifies the current technologies used to generate essential measures of pavement condition as “current best practice” and also shows currently deployed alternative technologies. Each of the current technologies is considered to be more accurate than the alternate technology at this time. However, advancements in the alternate technologies are being made at a rapid pace and in time these will have equal or better accuracy than current practices. Improved Access to Data In conjunction with advances in pavement data collection technology, the ability to share data has greatly improved. Pavement data are shared easily in internal or cloud-based databases or in customized access software. In the future, data to share and link to other data will be geo-tagged, which will allow any specific pavement data to be easily located and migrated to a pavement data layer(s) in a GIS. It is possible that automated data collection and analysis will also lead to more rapid access to the collected data. The integration of pavement data into GIS allows the ability to see interrelationships and have access to a full range of pavement data at any time (e.g., maintenance history, performance over time). As a pavement layer within GIS, the data can also be compared to other infrastructure elements, such as utilities, to better understand performance and repair requirements. Improved Decisions Uses of pavement condition data are described throughout these guidelines, including in Chapters 2, 4, and 5. Several technologies have the potential to contribute to improved decisions by providing more accurate pavement condition data or greater volumes of useful data. The ability to identify distresses with 3D laser imaging, for example, does not currently result in more accurate data than that identified by manual surveys. Manual surveys performed by trained personnel better distinguish between distress severity levels and identify some dis- tresses that are not discernible from 3D imaging, but automated surveys offer the following advantages: • Combination of automated and manual interpretation to increase accuracy, • Ability to collect data without sampling, Pavement Data Current Best Practice Alternate Technology Distress Manual condition survey 3D laser imaging LiDAR Vehicle-mounted camera UAS-mounted camera Deflection FWD Traffic speed or rolling wheel deflectometer Roughness Inertial profiler N/A Friction CFME N/A Table 18. Best practices (in terms of accuracy) and available alternate technologies for pavement data collection.

Next Generation Pavement Condition Data 95 • More accurate measurement of transverse profile, and • Measurement of longitudinal profile (not currently a part of a typical pavement condition survey). Note that a current ordering of pavement distress data accuracy places manual condition surveys first, followed by 3D imaging-based surveys, and then surveys performed by LiDAR and sUAS. Some vendors are bundling these technologies to further improve their data collection and processing capabilities. This order could certainly change over time as new technologies are developed and existing technologies undergo further refinement. All of the automated or semi-automated distress data collection methods have the ability to generate greater volumes of data by virtue of their ability to collect data over 100 percent of a pavement surface almost as easily as they can collect over a subset. Where manual surveys are mostly based on sampling, especially at the network level, these other survey methodologies potentially generate much more pavement distress data. Two relatively new pavement deflection measurement technologies also generate more data on pavement structural condition. These include devices that measure continuous pavement deflections and FWDs, which operate more quickly. Although they generate more data, their usefulness for airports has yet to be demonstrated. At most airports, the structural condition of pavements is assessed with a heavy FWD that can simulate the loads applied by the heaviest aircraft. An intriguing aspect of these new technologies is the application of computer vision to resolve and extract pavement distresses and conditions. For example, there exist a host of applications that are able to extract cracking attributes for asphalt pavements from 3D images without human intervention, as well as rutting and patching (Koch et al. 2015). Research in this area is providing almost continuous improvements, and many vendors are already using these tech- nologies with varying degrees of accuracy and success. The application of computer vision to extract pavement condition data that are collected via automated means holds immense promise to improve data accuracy, reduce data collection costs, and shorten analysis time. With access to more data and more accurate data, it is suggested that airports and their engi- neers have the ability to make better decisions. Together these lower the risk that conditions are not recognized, are not captured, or are misinterpreted. Recommendations for Additional Research As technology continues to advance, there are many additional areas for research that can advance all aspects of pavement condition data, from collection to use to access. These are iden- tified in the following suggestions for additional research topics: • Comparisons between the accuracy of different data collection methodologies and whether the differences are meaningful. • Development of appropriate pavement metrics for non-standard condition survey techniques, such as a video PCI and digital PCI. • Development of an appropriate metric (e.g., analogous to the IRI for roads) for airfield pavement roughness based on inertial profilers. Address the distinction between aircraft performance and passenger comfort, including the significance of passenger comfort in the airport environment. • Consideration of non-standard metrics in FAA requirements for reporting pavement conditions. • Examination of the effectiveness of decisions made via automated data collection. • Evaluation of the cost/benefit of these emerging data collection technologies.

96 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports As new technologies are deployed, it can be anticipated that there will be challenges to integrate similar data collected using existing methods. For example, the airport that has collected many cycles of pavement condition data by manual surveys may find that the same type of data collected with an automated technology does not match up well with the pre- vious data. This will impact analyses such as future performance modeling and CIP planning. As changes are made, guidance will be needed as to how to address such issues.

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“Pavement condition data” are essential inputs to the process of managing airport pavements and ensuring safe operations. The technology available today to collect pavement condition data is considerably different from that available even 20 years ago, and new technologies are being developed and introduced into practice at a rapid pace.

ACRP Research Report 203: Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports provides guidance on the collection, use, maintenance, and application of pavement condition data at airports. Such data include conditions that are visually observed as well as those that are obtained by mechanical measurement or other means. Visually observed distresses on a pavement surface (such as cracking, rutting, patching, and spalling) are widely used and accepted as indicators of pavement performance.

A key part of the background study leading to this report was the development of case studies of seven airports or airport agencies on their experiences with pavement data collection, use, and management. They include: Houston Airport System (Houston, Texas), Salt Lake City Department of Airports (Salt Lake City, Utah), Dublin International (Dublin, Ireland), Columbus Regional Port Authority (Columbus, Ohio), Gerald R. Ford International Airport Authority (Grand Rapids, Michigan), North Dakota (statewide), and Missouri (statewide).

Additional Resources:

  • An Appendix with case studies of airports and agencies based on responses to the project survey, the experience of the project team, and input from the ACRP project panel.
  • This presentation template is based on the content of ACRP Research Report 203. It provides information on airport pavement condition data collection, use, and storage that can be customized by a presenter to cover a subset of the overall ACRP report.
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