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

Chapter: Chapter 3 - Condition Data Types and Collection Methods

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Suggested Citation:"Chapter 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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 3 - Condition Data Types and Collection Methods." 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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15 This chapter examines the different types of airport pavement condition or performance data that are commonly used to monitor airport pavement performance and make decisions about pavement needs. It also looks at the different ways in which pavement condition data are collected. Types of Pavement Condition Pavement Distress The term “pavement condition” is often thought of as synonymous with pavement distress, although it is more accurate to think of pavement distress as one of several measures of pave- ment condition. For the purposes of this project, pavement distress refers to those indicators of pavement performance that are visible or measurable on the pavement surface. Other than the specific conditions spelled out in AC 150/5200-18C affecting safe aircraft operations, accepted measures of visual pavement condition are described in ASTM D5340. This standard “covers the determination of airport pavement condition through visual surveys of asphalt-surfaced pavements, including porous friction courses, and plain or reinforced jointed portland cement concrete pavements, using the PCI method of quantifying pavement condition.” The ASTM PCI standard identifies and defines each distress and describes how the distress is rated and measured. Table 4 summarizes this key information for flexible and rigid surfaces. Additional content in this standard describes how to conduct a pavement condition survey and how to convert the measurement of distress severities and quantities for a pavement into its PCI value. There are several different systems for evaluating roadway pavement condition [see, for example, FHWA 2014, the ASTM standard for roadways and parking lots (ASTM D6433), AASHTO distress protocols, and the many agency-specific procedures]. In the airport engi- neering community, the ASTM PCI stands alone as the preferred procedure. It has been used to evaluate airfield pavements in the United States since the late 1970s, and when the PCI procedure is followed, the results are accepted as accurate and repeatable. PCIs can be tracked over time or compared from one airport to another with confidence that observed trends and comparisons are real and meaningful. One alternate procedure for rating pavements is available, however, for a fairly limited use. FAA AC 150/5320-17A, Airfield Pavement Surface Evaluation and Rating Manuals, describes the PASER (Pavement Surface Evaluation and Rating) process for rating airport pavement sur- face conditions. This process produces a visual rating of the pavement surface on a scale from excellent to failed, but as noted in the AC it is not intended to be used as part of a PMP or to C H A P T E R 3 Condition Data Types and Collection Methods

16 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports determine the need for or timeliness of pavement rehabilitation. Rather, the PASER rating can provide information used in the airport safety data program and must be conducted annually. As discussed in Chapter 2, to document pavement conditions the FAA requires compliance with AC 150/5380-7B for projects funded through the AIP and with passenger facility charge (PFC) revenues. This AC requires detailed annual inspections be conducted, as well as less rigor- ous daily, weekly, and monthly inspections. The intent of this requirement is to ensure that airport agencies are thoroughly monitoring the condition of their pavements. When airport management, engineering, and maintenance staff have in-depth knowledge of pavement conditions, they are better able to manage their pavement network in a safe and efficient manner. If a condition inspection is carried out in compliance with ASTM D5340, the detailed inspections can be per- formed at a 3-year interval. There is little guidance for what procedures should be followed during the detailed annual inspections, so many airports opt to perform PCI inspections every 3 years. Generally, if pavement conditions are not changing quickly, the time between PCI inspections can be extended and still meet FAA requirements. One method for achieving this would be to perform PCI inspections every fifth year, with detailed annual inspections during the interme- diate years. However, abandoning PCI inspections entirely is not recommended, as PCI proce- dures yield a discrete value that quantifies pavement conditions and a PCI value is often required to justify the rehabilitation of a pavement section. Longitudinal Profile A pavement’s longitudinal profile is a measure of the deviations in the elevation of the pave- ment surface. While it is the profile that is measured, the results are interpreted to be reported either as pavement roughness or smoothness, and these terms can be considered to be roughly Flexible Pavement Distresses Rigid Pavement Distresses Distress Severitya Measureb Distress Severitya Measurec Alligator cracking L, M, H SF Blowup L, M, H Slab Bleeding N/A SF Corner break L, M, H Slab Block cracking L, M, H SF Longitudinal, transverse, and diagonal crack L, M, H Slab Corrugation L, M, H SF Durability cracking L, M, H Slab Depression L, M, H SF Joint seal damage L, M, H Sample Jet blast erosion N/A SF Small patching L, M, H Slab Joint reflection cracking L, M, H LF Large patching and utility cuts L, M, H Slab Longitudinal and transverse cracking L, M, H LF Popouts L, M, H Slab Oil spillage N/A SF Pumping L, M, H Slab Patching and utility cut patching L, M, H SF Scaling L, M, H Slab Polished aggregate N/A SF Settlement or faulting L, M, H Slab Raveling L, M, H SF Shattered slabs/intersecting cracks L, M, H Slab Rutting L, M, H SF Shrinkage cracking N/A Slab Shoving L, M, H SF Spalling (joint) L, M, H Slab Slippage cracking N/A SF Spalling (corner) L, M, H Slab Swell L, M, H SF Alkali-silica reaction L, M, H Slab Weathering L, M, H SF aSeverities are rated either as low (L), medium (M), or high (H), or as not applicable (N/A) if no severity level is defined. bFlexible pavement measures are either linear feet (LF) or square feet (SF). cRigid pavement distresses are either assigned to a slab or to a sample unit. Table 4. ASTM PCI distresses, severity levels, and measurement units.

Condition Data Types and Collection Methods 17 synonymous. One difference is that during construction, the emphasis is on measuring smooth- ness for quality control and quality assurance, and it is defined as deviations in the surface profile as measured by a profilograph and reported as a profile index, and also as measured at discrete locations with a 12-foot straightedge. Both transverse and longitudinal profiles are measured as part of a construction monitoring program, but because of where the measurements are taken, these are solely a reflection of construction quality. It is assumed that meeting FAA construction specifications for smoothness will produce an acceptable longitudinal pavement profile. For in-service airport pavements, pavement roughness is a more appropriate term (as noted in FAA AC 150/5380-9, Guidelines and Procedures for Measuring Airfield Pavement Roughness). No pavement surface is expected to be uniformly smooth, but in the airport context, pavement surfaces should be “free of surface irregularities that can impair safe operations, cause damage, or increase structural fatigue to an airplane” (FAA AC 150/5380-9). It is worth noting that in the context of highway pavements, the focus is on roughness and its effect on the comfort and safety experienced by the driver or passenger, while in the airport con- text, the focus is on the aircraft’s response to surface profile deviations and passenger comfort is less of a concern. The primary goal is to limit airport pavement roughness to a level where it does not impair safe operations or damage or increase structural fatigue of aircraft. For longitudinal profile documentation, the FAA requires compliance with AC 150/5380-9 for projects funded through the AIP and with PFC revenues. This AC outlines procedures to evaluate the longitudinal profile of a runway for single event (single location) bumps via Boeing Bump procedures, and the FAA offers the ProFAA tool to assist practitioners in ana- lyzing profile data and generating designated outputs. There is not a specified frequency for collecting longitudinal profile data. Runways should be tested if roughness is reported by pilots, as the Boeing Bump was developed from pilot feedback, or if changes in the profile are noted. The profile roughness (long wavelength roughness) is not evaluated by FAA procedures but should be accounted for during design. Profile roughness can apply fatigue to various airplane components. The profile roughness of the runway is not expected to change over time, other than as a result of construction or significant variable settlement or heaving. Surface Characteristics Runway surface characteristics can be documented by the friction values, groove measure- ments, and texture measurements. Where longitudinal profile can be thought of as a measure of long wavelength irregularities in profile (also referred to you as megatexture), which are inter- preted by aircraft and vehicles as roughness, surface characteristics are very short wavelength irregularities. Common terms that describe surface characteristics are microtexture, or the irregularities or texture on the surface of aggregates in a flexible or rigid pavement surface, and macrotexture, which is the unevenness of the surface caused by texturing, grooving, and so on. Together, microtexture and macrotexture are important because they contribute to skid resistance. A schematic of surface characteristics representing both short and long wavelength variations is shown in Figure 7. A special instance of surface characteristics on airport pavements is grooved pavements. Where runways and high-speed taxiways are grooved, the grooves themselves contribute to the overall macrotexture of the pavement. Construction of pavement grooves are addressed in AC 150/5370-10. During construction, the FAA requires groove depth, width, and spacing compliance with AC 150/5320-12C for projects funded through the AIP and with PFC revenues. If grooves are constructed to specification, there is not a need to evaluate the groove dimensions frequently.

18 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Monitoring grooves is discussed in AC 150/5320-12C and -12D (Draft), section 3–5. The AC calls for periodic monitoring of groove dimensions, with corrective measures required when “40 percent of the grooves in the runway are equal to or less than 1/8 inch in depth and/or width for a distance of 1,500 ft. . . .” It does not offer information on where to measure the grooves laterally or how to measure the grooves. In addition to manual measurements, either of a statistically representative sample or of all grooves, techniques based on laser scanning of the pavement surface have been used to provide rapid measurement of groove dimensions, and their use would be appropriate in trying to collect a large number of groove measurements for analysis (Li et al. 2018). The FAA does not require compliance with AC 150/5320-12D (Draft) for testing friction char- acteristics; however, there are recommended guidelines. The recommended frequency of testing is dependent on the number of daily jet aircraft landings per runway end as shown in Table 5. Each end of a runway should be evaluated separately to account for varying levels of rubber deposits. If the traffic mix for a runway end has 20 percent or more wide-body aircraft, the FAA recommends using the next higher-level testing frequency. After sufficient data are gathered on a runway end, the frequency of testing can be adjusted accordingly. Friction testing should be frequent enough to detect marginal friction values but not so frequent as to waste resources. Macrotexture depth © 2018 Applied Pavement Technology Figure 7. Microtexture, macrotexture, megatexture, and longer wavelength surface profiles. Daily Minimum Turbojet Aircraft Landings per Runway End Minimum Friction Survey Frequency ≤ 15 1 Year 16–30 6 Months 31– 90 3 Months 91–150 1 Month 151–210 2 Weeks > 210 1 Week Table 5. Recommended friction survey frequency (modified from FAA AC 150/5320-12D [Draft]).

Condition Data Types and Collection Methods 19 In most instances, the cause of low friction values will be excess rubber deposits. While texture measures are not required at a specified frequency, if appropriate friction values are not met and the reason for low friction values are not clear, texture measures are needed. Texture measure- ments can be conducted following the procedures in ASTM E 965-15 or ASTM E 2157-15. Structural Condition The structural condition of a pavement is the measure of the pavement’s ability to carry the loads to which it is subjected. Those loads are typically the aircraft that operate on the pavement, but occasionally other heavier vehicles (such as fueling trucks, aircraft rescue and fire fighting vehicles, maintenance vehicles, and so on), operating on a pavement designed for lighter aircraft, may have more of an effect on the structural condition of an airfield pavement than those aircraft. The structural condition or structural performance of an airport pavement is evaluated both indirectly and directly. A widely used indirect measure of structural condition is the measure of load-related distresses. This is the subset of the ASTM D5340 distresses that are directly caused by pavement loading, shown in Table 6. These are used to calculate a Structural Condition Index or SCI, which is the PCI calculated solely with the deduct values associated with load-related distresses. Another widely applied assessment of the structural condition of a pavement is measurement of the deflection of the pavement caused by an impulse load of similar magnitude and duration as that resulting from a moving aircraft. The maximum deflection recorded at the pavement sur- face indicates the relative strength of the pavement structure and distinguishes between strong and weak pavements. Pavement deflections are also used as inputs to procedures that produce measures of struc- tural condition as outputs. Examples of this are shown in Figure 8. A direct assessment of a pavement’s structure is achieved by sampling the pavement and then measuring its properties, and in particular its strength, through destructive testing. For reporting of PCNs, the FAA requires compliance with AC 150/5335-5C—for projects funded through the AIP and with PFC revenues—for pavements with bearing strengths of 12,500 pounds or greater. Paved public-use runways at Part 14 CFR 139 airports are also required to report a PCN value. In addition, when new projects are completed with AIP or PFC fund- ing, the PCN and allowable gross weight must be reported. There is not a specified period over which a PCN will remain valid, but if the pavement has been deemed structurally sound, visual indications of structural deficiencies have not developed, and the traffic mix remains unchanged, additional structural analysis is not necessary. Although not an FAA requirement, in most instances the structural capacity of a particular pavement should be cursorily examined at least every 10 years to ensure the site conditions and Flexible Pavement Rigid Pavement Alligator cracking Corner break Joint reflection cracking* Longitudinal, transverse, and diagonal crack Rutting* Pumping Settlement or faulting Shattered slabs/intersecting cracks Spalling (joint)* Spalling (corner)* *Distress may be load-related or attributed to other non-load causes. Table 6. ASTM D5340 load-related distresses.

20 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports traffic mix have not created significant changes in the pavement’s structural condition since the last evaluation. In addition, the methods used to evaluate the structural capacity of a pavement section are changed periodically. For comparison purposes it is suggested that all pavements within an agency be evaluated using the same procedures under similar general conditions (such as temperatures and base/subgrade saturation). Pavement Condition Collection Methods The technologies available to collect pavement condition data are expanding and becom- ing increasingly complex. Where 40 years ago the most commonly used techniques involved visual evaluations and destructive measurements, today pavement condition data collection is increasingly technology-driven and nondestructive. The positive impact of these changes is the generation of a greater quantity of data that is more accurate and, in many instances, available more rapidly and under more operational constraints. Increasingly, pavement condition data are georeferenced so that their location can be evaluated in relation to many of the factors that may affect conditions. In many instances, advancements in technology also contribute to safer data collection operations. However, for some condition measures there are competing technol- ogies and it has not yet been established which technology is the most accurate or cost effective. Pavement Distress ASTM D5340 is the definitive guide to defining pavement distress. It describes a standard in which distresses are identified, measured, and mapped by walking the pavement and using a measuring wheel, straightedge or string line, and scale, as shown in Figure 9. Throughout this standard, it is obvious that this procedure has been developed to be applied as a manual, visual inspection. The visual information (i.e., distress type, severity, and extent) is collected either on a paper form or a handheld electronic device such as a tablet or handheld computer. While the PCI was developed to be performed as a visual inspection procedure and the pro- cedure describes the manual collection of PCI distresses, technology is available today to collect and even process many of the PCI distresses in an automated or semi-automated manner. Aerial Condition Survey The ability to mount a downward-facing camera on an airplane or other airborne vehicle and capture digital images of pavement conditions has been available for a long time. The resulting © 2018 Applied Pavement Technology Figure 8. Relationship between deflection data and uses (BAKFAA, FAARFIELD, and COMFAA are airport design software programs).

Condition Data Types and Collection Methods 21 product is two-dimensional, so most distresses with a depth or “z” component (e.g., rutting, shoving, depression, faulting) cannot be easily identified. The usefulness of this technology is also constrained by the resolution of the image, which is a function of the camera, lighting condi- tions, and speed and height at which the image is collected. A fairly recent development is the use of unmanned aerial systems (UAS), or small UAS (sUAS), or drones, equipped with cameras, to carry out pavement photo and video inspec- tions. A discussion of the issues associated with operating UAS in controlled airspace is beyond the scope of this document. However, even with the regulatory restrictions that are currently in place, UAS are already being deployed to evaluate pavement conditions. These devices can be programmed to fly an autonomous flight path and can survey a typical commercial airport runway 8,000 feet long in less than 1 hour (where a similar, complete PCI survey performed manually could take up to 16 hours by a two-person crew, depending upon pavement condi- tions). As part of ACRP Project 03-42, “Airports and UAS,” in August 2017 a demonstration was conducted at Front Range Airport in Colorado on the use of sUAS within controlled airspaces. Digital images of pavement conditions were collected in a drone survey and compared to the collection of pavement distresses by manual methods. Several firms are known to be undertaking proof of concept studies on the use of UAS at airports independently of ACRP. In March 2017, a U.S.-based consulting firm used a Topcon Falcon 8 to collect 630 photos in 20 minutes along 3,000 feet of Runway 9L/27R at Hartfield- Jackson Atlanta International Airport. The images were then post-processed to plan for future pavement rehabilitation (UAS Magazine 2017). The images shown in Figures 10 and 11 were recorded with a properly permitted sUAS at a GA airport in Maine. Cameras available today provide a very high-resolution image (1 pixel = 0.1 inches), making the manual identification and measurement of many pavement distresses and severity levels feasible. However, as with the other imaging technologies, the major technological advance- ments lie in the ability to interpret distress types and severities through software algorithms based on an interpretation of the pixels. © 2018 Applied Pavement Technology Figure 9. Manual visual condition survey using a measuring wheel to record the dimensions of a distress.

22 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports © 2017 HTA Figure 10. Apron area captured by an sUAS. Figure 11. Condition of taxiway as viewed from an sUAS. © 2017 HTA Light Detection and Ranging (LiDAR) LiDAR sensor-equipped ground-based vehicles, supported by location-referencing systems, generate a point-cloud image of the pavement surface. Figure 12 shows a typical LiDAR-equipped van, with additional digital cameras, distance measuring equipment, and GPS. Figure 13 shows two point-cloud images (not photographs) of a runway surface. The point cloud can then be analyzed as shown in Figure 14 to extract distress quantities as well as many other airport features that can be identified from the data. Three-Dimensional (3D) Laser Imaging 3D-laser imaging is a widely used technology for collecting and analyzing pavement distress data on roadways but is currently used much less frequently on airfield pavements. This vehicle- mounted technology uses multiple laser line scans to generate a 3D image of the pavement

Condition Data Types and Collection Methods 23 Images © 2014 Woolpert Figure 12. LiDAR-equipped vehicle showing LiDAR sensors and a schematic of associated hardware (MMS = mobile mapping system). © 2018 Woolpert Figure 13. LiDAR images of runway surface.

24 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports © 2018 Woolpert Figure 14. Interpretation of LiDAR data to identify pavement condition and other assets. surface that can be captured at traffic speeds and under any lighting conditions, although it cannot be operated when the pavement surface is wet. While earlier versions of this technology provided a 2D image that made it difficult to identify and rate some pavement distresses, current 3D capabilities include the ability to resolve surface conditions at approximately 0.039 inches, making it possible to identify distresses such as raveling and to distinguish between distress severity levels. Figure 15 is a graphic of a van equipped with 3D-laser imaging technology and Figure 16 is a side-by-side comparison of a pavement image captured with 3D-laser imaging (left side) and 2D-laser imaging (right side). In the 3D image it is possible to identify and resolve crack severity levels as well as rate surface characteristics, while this is not as easy to accomplish in the 2D image. Vehicle-Mounted Camera Surveys The same types of cameras that are used in aerial surveys can be mounted on ground-based vehicles and used to collect pavement condition data. One advantage of mounting these cam- eras on a vehicle is that being closer to the ground provides higher resolution images. A pri- mary disadvantage is their limited ability to capture all pavement distresses, or to distinguish among severity levels. Often different types of cameras are mounted on data collection vehicles to supplement the information collected using LiDAR or 3D-laser imaging. These cameras, when pointed beyond the pavement, can provide useful information about other airport infrastructure elements.

Condition Data Types and Collection Methods 25 © 2018 PMS Figure 16. Side by side comparison of a 3D pavement image (left) and 2D image (right). © 2018 Applied Pavement Technology Figure 15. Ground vehicle equipped with 3D-laser imaging and other hardware. Discussion of Pavement Distress Technologies The applicability of various technologies available to complete pavement distress surveys is one of the more contentious issues among those engaged in providing and using the collected information. Each of the approaches discussed above has advantages and disadvantages, not the least of which is the varying degree to which each complies with prescribed FAA standards. In addition to the issue of compliance, however, are important considerations regarding the cost of

26 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports the different approaches, the usefulness of the results, the speed with which the information can be collected and analyzed, the impact on operations of data collection, and the accuracy of the information. The visual condition survey is the only method of completing a pavement condition survey that strictly complies with the ASTM D5340 methodology. This is in part a function of how the ASTM standard is written, the included distresses, and the manner in which the severity levels are identified. While the visual condition survey offers the highest level of accuracy, it is labor intensive and potentially time-consuming, may have the greatest adverse impact on airfield operations (depending on the sampling rate), and may be more costly than other data collection techniques. It is either performed in daylight or under artificial illumination. One aspect of applying these technologies is the assumption of repeatability (without even addressing their accuracy). If weather conditions are suitable for data collection, specific pave- ment areas can be directly compared and evaluated over multiple inspections. In contrast, the visual condition survey based on ASTM D5340 has a well-defined set of rules that can be applied differently, either by the same rater at different times or by different raters. A variety of factors (e.g., interpretation of ASTM D5340, weather conditions, lighting, human factors, etc.) can have an impact on the repeatability of a visual condition survey. To increase the repeatability of visual condition surveys, it is suggested that inspectors take ample photographs and notes to supplement the collected data. Aerial surveys by sUAS are not yet able to provide all of the information on pavement dis- tresses that can be obtained by a visual or manual survey, but they enable the collection of pavement images in a fraction of the time needed to perform a manual survey. Such surveys are useful for frequent inspections where it is difficult to quickly inspect the entirety of a pave- ment’s surface. The output from an aerial survey is also particularly useful for mapping crack sealing and patching maintenance needs. In fact, unless a PCI survey covers 100 percent of the pavement surface and includes distress mapping, the results do not lend themselves well to the development of precise maintenance repair quantities and locations, whereas an aerial survey is ideal for that purpose. It is fairly easy and inexpensive to deploy the equipment needed to complete an sUAS pavement survey. Disadvantages include the barriers raised by permitting and operating sUAS in restricted airspace, the technology’s inability to resolve all pavement distresses, and the difficulty in evaluating pavement conditions beneath obstacles such as parked aircraft, jet bridges, and the like. Furthermore, since the collected images do not characterize all dis- tresses according to the ASTM standard, it will not provide a true PCI. This affects both the accuracy of the PCI value and the ability to compare a PCI obtained in this manner to PCIs obtained by following the ASTM standard. LiDAR equipment is van-mounted and has the ability to generate a large volume of pavement condition information from a vehicle traveling at safe operating speeds. While it is not possible to conduct a complete PCI survey with LiDAR, it has been used in conjunction with ground truth inspections (physical measurement of what was recorded by other technologies) to evaluate pave- ment conditions. LiDAR data require extensive computer storage capabilities, and this aspect must be considered by whoever is going to “own” the data. LiDAR is most effectively used when otherwise needed for another purpose, such as to capture survey data or other assets. LiDAR equipment does not function well in the presence of fog, rain, snow, smoke/dust, and so on. 3D-laser imaging, when combined with non-contact sensors to record transverse profile, comes closest to being able to record distresses in accordance with the ASTM D5340 defini- tions. Its capabilities contrast with 2D-laser imaging, which only measures distress intensity and not depth. Distresses that are problematic to identify with 2D-laser imaging are those with

Condition Data Types and Collection Methods 27 a third (vertical) dimension, such as raveling and weathering, faulting, rutting, and depressions. Perhaps more important than being able to capture this information is the work done by researchers and vendors to develop software that will read the images and correctly interpret the results as PCI distresses. This is an ongoing and developing process, with researchers already claiming to be able to use machine vision to interpret between 50 and 100 percent of ASTM- defined pavement distresses. An advantage of automated data collection methods (e.g., sUAS, LiDAR, and 3D imaging) is that they can be used to collect data on 100 percent of the pavement surface whether those data are used or not. They also provide a permanent record of conditions at a given time that can be revisited for verification or other purposes. The data can also be georeferenced so that locations are matched up with the observed conditions. With digitally encoded data, the results can be interpreted using specially developed software tools. This capability holds the promise of making the collection and analysis of pavement condition data for PMPs a partially automated process that can be completed in a fraction of the time compared to what is currently needed to perform such studies. Finally, and especially with LiDAR and 3D imaging, there is the ability to capture information about other infrastructure elements at the same time the pavement condition is being evaluated, including drainage, lighting, signage, and pavement markings. At the same time, a general drawback that may become increasingly significant is the difficulty of switching between various distress collection technologies. There is no widely accepted basis for comparison between technologies and it is unlikely that different technologies applied to the same pavement will yield the same pavement condition results. Even the deployment of different “generations” of the same equipment may yield inconsistent results. If there are inconsistencies in the ability to identify and resolve pavement distress, then there will also be inconsistencies in the analyzed results, which, in turn, could lead to inconsistencies in the decisions made from those results. Each technology also is associated with different abilities to resolve certain distresses. One commonly cited resolution is crack width: a technology is said to be able to identify cracks down to a width of 0.039 inches or less. With constant changes in these capabilities, the user should understand the limitations of the currently available technologies and determine what capabilities are needed. Tables 7 and 8 identify distresses that can generally be captured by various data collection methods for asphalt- and concrete-surfaced pavement, respectively, and meet ASTM D5340 distress criteria. Site-specific limitations in some instances may affect the usefulness or appli- cability of each data collection method. Also, some technologies may be able to identify the presence of a distress, but not its severity level. A topic for further study that is beyond the scope of these guidelines is whether ASTM D5340 should be revised, whether a new ASTM standard should be developed outlining a procedure for using remote-sensing technology, or whether the requirements of AC 150/5380-7A and the defi- nitions in AC 150/5380-6C should be altered to align with the condition information that is col- lected by remote-sensing technology. As discussed in Chapter 8, the result could be a digital PCI, in which it is understood that distresses were measured and a PCI was calculated using alternate methods. On the roadway side, there are already AASHTO standards for distresses measured by means of automated data collection on asphalt-surfaced pavements (AASHTO R85; AASHTO R87). For example, AASHTO R 85-18 addresses the identification of cracks in asphalt pavement while AASHTO R 87-18 provides guidelines for calculating rut depth from a transverse profile. Longitudinal Profile There are many different ways to measure longitudinal profile, and the purpose of measure- ment is often aligned with the use of specialized equipment or analytical tools. For example,

28 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Distress Manual Laser Imaging LiDAR UAS Imaging Alligator cracking Low Severity Medium Severity High Severity Low Severity1 Medium Severity High Severity Low Severity2 Medium Severity High Severity Low Severity2 Medium Severity High Severity Bleeding Severity N/A Severity N/A3 Severity N/A Severity N/A3 Block cracking Low Severity Medium Severity High Severity Low Severity4 Medium Severity4 High Severity4 Low Severity2 Medium Severity High Severity Low Severity2 Medium Severity High Severity Corrugation Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Depression Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Jet blast erosion Severity N/A Severity N/A Severity N/A Severity N/A Joint reflection cracking Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity2 Medium Severity High Severity Low Severity2 Medium Severity High Severity Longitudinal and transverse cracking Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity2 Medium Severity High Severity Low Severity2 Medium Severity High Severity Oil spillage Severity N/A Severity N/A Severity N/A Severity N/A Patching and utility cut patching Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity5 Medium Severity5 High Severity Polished aggregate Severity N/A Severity N/A Severity N/A Severity N/A Raveling Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Rutting Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Shoving Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Slippage Severity N/A Severity N/A Severity N/A Severity N/Acracking Swelling Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Weathering Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity 1Data collection method applicable if wheelpath location is known. 2Data collection method applicable if cracks have been sealed. 3Data collection method applicable if bleeding is not scattered and uniformly distributed. 4Full extent of block cracking may be difficult to document without image stitching. 5Data collection method applicable where there are color differences between patch material and original asphalt. Table 7. Ability of different data collection methods to identify asphalt-surfaced pavement distresses according to ASTM D5340 standards. pavement contours have traditionally been generated with a rod and level as part of a rehabilita- tion design or to better understand cross slopes and drainage patterns. A walking profiler may also be used to generate a precise profile elevation along a set path. Inertial profilers meeting the requirements of ASTM E950 Class 1 are the typical equipment used today to provide a measure of pavement roughness or to calculate a Boeing Bump or Boeing Bump Index. The Boeing Bump parameter considers bump heights and lengths as they

Condition Data Types and Collection Methods 29 generally impact aircraft operation and performance. Further explanation of this measure, as well as FAA guidance on using pavement roughness data, are found in AC 150/5380-9. The typi- cal operating configuration to collect profile data is to mount a sensor bar with an infrared laser sensor or sensors on the front bumper of a vehicle and, with accelerometers, distance measur- ing equipment, and an onboard computer, record the pavement profile to within 0.002 inches at a sampling rate up to every 0.5 inch. Figure 15 shows the location of the sensor bar on a van equipped with other pavement condition data collection hardware. An Innovative Pavement Research Foundation (IPRF) report (Gerardi et al. 2007) provides a good overview of profile measurement on airfield concrete pavements, which is largely also Distress Manual Laser Imaging LiDAR UAS Imaging Blowup Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Corner breaks Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity1 Medium Severity High Severity Low Severity1 Medium Severity1 High Severity Longitudinal, transverse, and diagonal cracks Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity1 Medium Severity High Severity Low Severity1 Medium Severity1 High Severity Durability cracking Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Joint seal damage Low Severity Medium Severity High Severity Low Severity2 Medium Severity2 High Severity2 Low Severity2 Medium Severity2 High Severity2 Low Severity2 Medium Severity2 High Severity2 Patching Low SeverityMedium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity3 Medium Severity3 High Severity Popouts Severity N/A Severity N/A Severity N/A Severity N/A Pumping Severity N/A Severity N/A4 Severity N/A4 Severity N/A4 Scaling Low SeverityMedium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Settlement Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Shattered slab Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity1 Medium Severity1 High Severity Shrinkage cracking Severity N/A Severity N/A5 Severity N/A Severity N/A Spalling Low Severity Medium Severity Low Severity Medium Severity Low Severity1 Medium Severity Low Severity1 Medium Severity1 High Severity High Severity High Severity High Severity Alkali-silica reactivity (ASR) Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity Low Severity Medium Severity High Severity 1Data collection method applicable if cracks have been sealed. 2Data collection method applicable if damage is from extrusion, vegetation growth, or sliver spalls. 3Data collection method applicable depending where there are color differences between patch material and original concrete. 4Data collection method may be applicable depending on extent of pumping. 5Data collection method may be applicable depending on characteristic of shrinkage cracking. Table 8. Ability of different data collection methods to identify concrete pavement distresses according to ASTM D5340 standards.

30 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports applicable to asphalt-surfaced airfield pavements. For those interested in calculating various measures of longitudinal profile, the FAA’s software, ProFAA, can be used to calculate the following profile indexes: straightedge, Boeing Bump, International Roughness Index (IRI), California Profilograph, and RMS Bandpass. Discussion of Longitudinal Profile Technologies In the highway environment, the IRI is recognized as a global standard. The IRI is a measure of the comfort of a passenger in a vehicle moving along a pavement, and many highway agencies use it as an input to the decision process of managing pavements. It is used in the World Bank’s pavement management models to allocate funds for the analysis, planning, and management of road projects and for investment decisions. It has also been adopted as a highway performance measure by the FHWA. Inertial profilers represent the best tool available to monitor or manage a pavement’s longitudinal profile because of their widespread availability, accuracy, repeatability, and operating cost. The IRI bears little relationship to the Boeing Bump Index, since the former measures the response to ride that would be felt by a vehicle passenger and the latter is used to identify responses that would be structurally damaging or operationally unsafe for an aircraft. While both are useful measures, monitoring, reporting, and taking action based on roughness are not common in the process of managing airfield pavements. However, while IRI values are not directly linked to airfield pavement actions, IRI may be used to anticipate pilot complaints or to provide an indication that the pavement is deteriorating to the point that maintenance or rehabilitation will be required. The FAA is researching the development of a new runway roughness index using data gen- erated by aircraft simulators, a B727 instrumented with an inertial profiler system, and a van equipped with an inertial profiler. The data generated are analyzed using ProFAA and compared to the results obtained with a rolling inclinometer (Larkin 2018). It is expected that the new index will be used to monitor in-service runway roughness and to recommend actions based on the measured results (FAA n.d.). Surface Characteristics Where longitudinal profile can be thought of as a long wavelength measure of the pave- ment surface, the surface characteristics discussed here are of very short wavelengths and refer to those factors that affect the ability of moving aircraft to maintain control and safely slow down or stop. As previously noted, monitoring the friction on runway pavements is accomplished with continuous friction measurement equipment (CFME), as described in AC 150/5320-12D. The FAA maintains a list of approved devices, which are those that meet the requirements of ASTM E670 (for side force friction measurement) or ASTM E2340 for fixed brake slip measurement. Often reported as skid resistance, a skid number, or a friction level, the output from the operation of CFME is used to determine whether it is safe to land on a runway. This testing is appropriate to assess wet weather skidding, loss of friction from snow/ice buildup, and loss of friction from rubber buildup. However, other pavement surface characteristics are also men- tioned in AC 150/5320-12. For example, pavement grooves should be checked when there is wear to determine if they are still able to prevent hydroplaning. No method of groove depth measurement is given, but the traditional method is to measure these by hand, as shown in Figure 17. More recent technology involves the use of non-contact lasers to generate these mea- surements. When inertial profilers are used for this purpose, the FAA’s software, ProGroove, can be used to evaluate pavement grooves.

Condition Data Types and Collection Methods 31 AC 150/5320-12 also identifies texture depth as a surface characteristic to measure when there are low friction values and the cause is not obvious. The manual method of measuring texture depth is with the sand patch test (ASTM E965). The Circular Track Texture Meter, or CT Meter (ASTM E2157), can also be used. Since these both are primarily measures of macro- texture, they provide information that can also be extracted from non-contact laser sensors, as described previously. And while the correlations are not yet well established, it may be possible one day to generate friction measurements from texture depth data. Discussion of Surface Characteristics Technologies The only approved method to measure and report on surface friction is by using CFME. Eventually, the use of laser and perhaps other non-contact sensing technologies may be able to measure both microtexture and macrotexture so that they can be used to monitor when intervention, such as rubber removal or retexturing, is required. In the meantime, non-contact sensors can readily collect groove dimensions and texture depth data, but unless this technology is already on site for other uses, the deployment of this technology is typically not cost effec- tive when compared to more manual methods, including hand measurement or the use of the CT Meter. Structural Condition The most common device in use today to measure a pavement’s structural condition is the FWD or HWD. These are designed to impart to the pavement an impulse load similar in magni- tude and duration to that of a moving aircraft and measure the deflection response of the pave- ment to that load. The FWD is composed of a load plate through which a dropped mass delivers a load to the pavement while individual geophones located at the bottom of the load plate and at specified distances away from the load plate measure the pavement response to the load. When layer thicknesses and material types are known, the moduli of individual layers and the subgrade support can be back-calculated from the deflection data. Even when accurate records are available, site variability means that cores are required to provide pavement layer thicknesses. On concrete pavements the equipment can also be used to calculate load transfer efficiency and to detect voids beneath the slab. Figure 18 shows an FWD with key components identified. © 2018 Applied Pavement Technology Figure 17. Hand measurement of runway groove depth.

32 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Upon its introduction over 40 years ago, the FWD represented a major advancement over previously used approaches to evaluating pavement structures, including use of a Benkelman beam and coring and testing. Whether vehicle-mounted or towed on a trailer, the device oper- ates by advancing to a test location where the frame with the load cell and geophones is lowered, the load is applied, the pavement response (measured by the geophones) is recorded, and the frame is lifted before advancing to the next test location. The most recent innovation in pavement deflection testing is the rolling weight deflec- tometer (RWD) or traffic speed deflectometer (TSD). Conceptually these devices represent a significant advancement in that deflection data are generated by a moving vehicle that applies the load and uses non-contact sensors to record deflections. These devices have been used in trials to evaluate highway pavements (at the national level by the FHWA) and refined for over 10 years, but have yet to see significant use on airfield pavements. One limitation in apply- ing this technology to airports will be that as currently configured these devices are unable to reproduce the tire pressures and landing gear configurations of large aircraft. Figure 19 shows the undercarriage of a tractor trailer where the reference beam and laser scanners are mounted. © 2018 Applied Pavement Technology Load plate Geophones Figure 18. Close-up of sensors on a trailer-mounted FWD. Source: FHWA Figure 19. Example of one RWD configuration.

Condition Data Types and Collection Methods 33 Discussion of Structural Condition Technologies For the present, the FWD represents the standard for evaluating airfield pavement structural capacity. The data output by an FWD provides inputs for project-level rehabilitation design, the determination of structural remaining life, and the calculation of PCNs. The primary drawback of using an FWD is that it measures point responses; 250 is about the typical number of points that may be surveyed in an 8-hour period (although this is dependent on the testing pattern, facility type, and access). As such, typical testing patterns will specify the spacing of points in critical locations (such as the aircraft wheelpaths) in order to generate an estimate of structural conditions at those points. Advantages to the use of the FWD include the established relation- ships between pavement response and the need for structural or nonstructural M&R strategies, as well as the ability to interpret testing results on jointed concrete pavements to report load transfer efficiencies and to identify voids. The higher speed data collection efforts made possible with the RWD or TSD represent the future of pavement structural condition assessment, although perhaps not for some time on airfield pavements. Their current value lies in their ability to operate at or near traffic speeds without traffic control and to collect continuous deflection data. They can provide useful network-level information to monitor overall conditions or to determine the need for more in-depth investigations. This capability lends itself to evaluating busy roadways, but does not fulfil a critical need on airfield pavements (access and safety). The lack of maneuverability would also be an issue on many short airfield pavements (such as connecting taxiways and taxilanes) and in apron areas, as would the need for higher loading levels on airfield pavements. A distinct advantage offered by continuous measurement of structural capacity is the ability to distinguish between weak and strong pavement areas, perhaps even before distresses appear on the surface. This ability should contribute to improved delineation of project boundaries and the development of appropriate maintenance and rehabilitation strategies. When such distinctions are identified, they could be used to trigger more intensive and targeted testing by other means. Summary Several types of pavement condition data are regularly collected and used to monitor per- formance and safety on airfield pavements. This chapter has covered the primary types of pavement condition data and described the devices and technologies that are available for col- lecting those data. In every instance there are examples of both currently used approaches and innovations that are either starting to be used on airport pavements, being used on roadways but not yet on airport pavements, or being researched. Some thoughts on implementing new technologies are presented in Chapter 8; the following chapter will go into greater detail on how pavement condition data are used.

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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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