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Suggested Citation:"Chapter 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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 7 - Data Collection Guidelines." 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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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

74 C H A P T E R 7 Deciding what pavement condition data to collect and how to collect them is a complex matter, as it is in part determined by the need to comply with FAA regulations and in part a func- tion of specific agency needs and practices. Certainly each airport agency needs pavement condi- tion data to manage their pavement assets, but as has been discussed previously, there is some latitude in which data are collected and how the data are used. This chapter presents decision trees, information on the value of condition data, and ways to improve the use of condition data so that the most effective data collection methods can be applied. Decision Trees To aid in selecting which data collection methods should be used, several decision trees are presented. Each decision tree is for a category of data use. The following categories are defined. • Data use for FAA compliance, • Data use by airport or agency management, • Data use by engineering or other technical departments, and • Other data uses. A starting point to determine which data collection methods are appropriate is to consider how the data will be used after collection. Based on the anticipated use(s) of the data and the other factors in each decision tree, an agency could use the appropriate decision tree to select possible data collection methods. Not every branch of each decision tree will need to be evalu- ated if the agency is not concerned with that specific data use. To use the decision trees, the following steps can be followed: 1. Decide how the data will be used. 2. Based on the desired use and airport characteristics, select the possible data collection methods. 3. Record the total occurrences for each data collection method. 4. Evaluate the most common available data collection methods. a. Determine if the most common data collection methods meet all of the specific uses or if a combination of data collection methods will be required for different data types. b. Identify what other factors related to data collection and use should be considered for the agency and their impact. c. Estimate the cost for data collection methods, including mobilization and value of associated condition data. A simplified example of a data collection method selection matrix for a small-hub airport is presented in Table 17. In this example, there are seven potential data uses for the airport related Data Collection Guidelines

Data Collection Guidelines 75 to FAA compliance, management decisions, and engineering or other technical departments. The most common data uses are shown in bold type and should be examined further. In this example, CFME friction data collection and FWD/HWD data collection should occur at the network level. For pavement condition data, both manual PCI inspections at less than the 95 percent confidence level and at or above the 95 percent confidence level should be evaluated. Both inertial profiling or a rod and level may be used to evaluate the longitudinal profile if conditions warrant. An additional example is provided in Appendix A for a large-hub airport. In this example, there are nine potential data uses for the airport related to FAA compliance, management decisions, and engineering or other technical departments. Factors Within Decision Trees Within most of the decision tree branches, the factor that determines which data collection methods are appropriate is the type of airport or agency. The type of airport is a reasonable proxy for the characteristics and capabilities of the agency. For example, a large-hub airport will likely include a proficient engineering department with ample resources, while an independent GA airport is more likely to have limited personnel and resources. The following types of airports are defined in the decision trees to simplify presentation of the data collection options: multiple air- port system, large hub, medium hub, small hub, statewide systems, and GA. If a multiple airport system does not have a small-hub airport or larger, it should be treated as a statewide system. Within the decision trees shown in Figures 35, 36, and 38 through 43, data collection methods *Secondary input as needed. The most common data uses are shown in bold type and should be examined further. Use Category FAA Compliance Management Engineering or Other Technical Departments Runway Friction Reporting Runway PCN Reporting Pavement Condition Reporting External Funding Justification APMS (Detailed) CIP Development Maintenance Planning CFME friction data collection FWD/HWD at project level FWD/HWD at network level Non-PCI pavement condition rating system by visual assessment Manual PCI < 95% confidence level Manual PCI 95% confidence level Manual PCI < 95% confidence level Manual PCI 95% confidence level FWD/HWD at network level Manual PCI < 95% confidence level Manual PCI 95% confidence level Manual PCI at 100% sampling Manual PCI < 95% confidence level Manual PCI 95% confidence level Aerial condition survey at 100% coverage FWD/HWD at network level Longitudinal Profile* (rod and level) Longitudinal Profile* (inertial) CFME Friction Data Collection* Non-PCI pavement condition rating system by visual assessment Manual PCI < 95% confidence level Longitudinal profile* (rod and level) Longitudinal profile* (inertial) Table 17. Example of data collection method selection for small-hub airport.

76 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports for pavement condition are shown in shaded dark green boxes while data collection methods for structural condition are shown in shaded orange boxes. Data collection methods for surface characteristics are shown in dark blue while data collection methods for physical characteristics are shown in shaded grey boxes. Inspection Density Background There are various options for the density of pavement inspection. Inspecting 100 percent of pavement will provide a very robust data set, but is labor intensive. Within ASTM D 5340, procedures are shown to determine the sampling rate to generate a PCI with a 95 percent con- fidence level. If the PCI of each section is confirmed to meet this confidence level, it is assumed the data has a sufficient level of accuracy to be used in most applications. Depending on the object of the data use, a PCI can be determined with a lower sampling rate that does not meet a 95 percent confidence level. The effort for this inspection will be less; however, the level of accuracy may not fit the needs of the agency. Pavement Condition Data Collection from Non-Manual Inspections Within the decision trees, data collection from an aerial survey, 3D-laser imaging, or LiDAR is referred to as non-PCI pavement condition rating systems. This is because without a complete ground truth inspection, these data collection methods do not fully comply with ASTM D 5340. A partial explanation of this shortcoming is that the definitions of nine of the sixteen dis- tresses for concrete pavements and five of the eighteen distress for asphalt-surfaced pavements in ASTM D 5340 partially rely on rating the extent of FOD potential. To properly rate the FOD potential for a distress, it is often necessary for an inspector to physically examine a distress to determine if material is in a loose or potentially loose state. In addition, there are certain dis- tresses, such as durability cracking, ASR, and raveling, that are exceedingly difficult to properly identify and assign an appropriate severity during a survey using one of the non-PCI methods. Not being fully compliant with ASTM D 5340 does not mean that these data collection methods should not be part of a pavement evaluation program. They can effectively be used to document pavement conditions; however, this data should not be referred to as “PCI” data. Instead, these metrics should be referred to as a “UAV-PCI,” “3D-PCI,” LiDAR-PCI,” or an equivalent name reflecting their collection method. A separate designation could even be used to identify whether the condition was interpreted manually, via automated means, or a combination of both. Addi- tional clarifying designations inform all users of the method of data collection and make it possible to distinguish between multiple data collection methods that an agency might use. Use for FAA Compliance For FAA Part 139 airports, complying with FAA ACs is paramount. In various ACs, the FAA requires reporting of runway friction data, runway PCN data, and pavement condi- tion data. Figure 35 shows the potential data collection methods that meet FAA compliance requirements. Runway Friction Data Reporting The FAA does not require compliance with AC 150/5320-12D for testing friction charac- teristics; however, the guidelines are recommended and are covered in Chapter 3. The only reliable method to test the surface friction characteristics is with CFME, and the FAA recom- mends that all airports with jet traffic either own or have access to CFME. There are several approved manufacturers of this equipment, and the acquisition of the CFME is eligible for funding through the FAA.

Figure 35. FAA compliance decision tree.

78 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Runway PCN Data Reporting As discussed in Chapter 3, the FAA requires compliance with AC 150/5335-5C for reporting PCNs 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 must also report a PCN. In addition, when new projects are completed with AIP or PFC funding, the PCN and allowable gross weight must be reported. There is no specified time during which a PCN will remain valid, but if there are significant increases in pavement use or changes in pave- ment condition, an update should be considered. The FAA also provides guidance for reporting changes to the PCN and gross weight within Form 5010. The FAA allows the calculation and reporting of PCN data without physical measure- ments or knowledge of the pavement structure based on the “using aircraft method.” In this approach, the highest airport classification number (ACN) of all aircraft that are currently permitted to use the facility is reported as the PCN. At the same time, the FAA discourages the use of this method because of significant inaccuracies associated with the process. The pavement capacity can be overestimated, which could lead to pavement damage, or the pave- ment capacity could be underestimated, which could lead to the unnecessary restriction of acceptable traffic. With a technical evaluation of the pavement strength, it is necessary to know the physical properties of the pavement structure. To have a definitive knowledge of an in-place pavement structure, FWD/HWD testing is needed, except with new construction projects. For multiple airport systems, large-, medium-, and small-hub airports, this testing can be performed at the network or project level as defined by AC 150/5370-11B. For a statewide system or GA airport it is recommended that this testing be performed at the network level. The level of detail with this density of testing is adequate to capture the physical properties of the pavement structure. Pavement Condition Data Reporting The requirements for reporting pavement condition data are outlined in Chapter 2. As previ- ously noted, however, AC 150/5380 distinguishes between non-PCI pavement condition rating systems, which must be performed annually, and manual PCI inspections, which can be sched- uled on a 3-year interval. The possible data collection methods for small-hub and GA airports shown in Figure 35 are the least labor intensive. The data collection methods are expanded for statewide systems and are further expanded for multiple airport systems, large-hub air- ports, and medium-hub airports. While larger airports do not require highly detailed data for FAA compliance, an airport may decide to collect additional information for other uses. It is assumed the smaller airports rarely have the technical resources or funding to collect or use detailed pavement condition data. Frequency of Pavement Condition Data Collection The frequency of data collection is specified for FAA compliance. Within the remaining data use categories, the frequency of required pavement condition data collection can vary from every 2 to every 5 years. This wide range reflects the relationship between the condition of the network and the rate of deterioration. The inspection frequency should be at an interval where most pavement sections will not experience a significant decrease in conditions between inspections and pavements in or approaching poor condition are documented. For a network mostly in good condition with pavements that have performed well to date, a 5-year interval will be sufficient to document the change in conditions. For a network with mixed conditions and pavements that deteriorate quickly, a 2-year interval is more appropriate to record condi- tions and to program and execute appropriate responses. As more inspections are completed,

Data Collection Guidelines 79 the number of data points to model pavement performance is increased, which also allows more accurate predictions of future conditions. These data can be used to refine the inspection frequency over time. Use by Management The primary management uses of pavement data are to justify funding needs externally, to create the long-term agency CIP/budget, or to update the master plan. At some airports, man- agement will directly use pavement data to make decisions, rather than the more common situa- tion in which various departments, including engineering, provide management with the inputs needed to make decisions. These data can significantly shape the future actions of the agency so access to appropriate data is imperative. Figure 36 shows the potential data collection methods for use by management. External Funding Justification Pavement data may be used externally to justify funding to the FAA, state governments, or local governments. Funding requests can be for any level of maintenance, repair, or rehabilitation. In order to justify funding, the condition of the pavement in the form of a recent PCI provides an established, objective, and widely accepted rating of the pavement condition. Depending on the specific nature of a funding request, structural condition data collected at the network level as defined by FAA AC 5370-11B may be used as additional justification. Structural condition data will generally be used in rehabilitation projects where aircraft loading exceeds structural capacity. Depending on project specifications, other types of data may be needed to justify fund- ing externally; however, the data collection methods present the minimum requirements. For multiple airport systems and large-, medium-, and small-hub airports, PCI data from a manual inspection at less than, at, or above a 95 percent confidence level are appropriate. Any of these sampling rates will provide justification of the pavement condition to an external govern- ing body. For statewide systems and GA airports, PCI data from a manual inspection at less than a 95 percent confidence level are appropriate. This level of data provides the external governing body with an objective measure of the pavement condition. A level of data collection requiring additional effort is not required. Long-Term Agency CIP/Budgeting or Updating Master Plan Management will also use pavement data to determine a long-term agency CIP/budget or when updating the master plan. These products often cover a period of 10 to 20 years. Only pavement condition data are included in this branch of the decision tree as the condition of pavement can be projected in future years, while metrics related to the surface characteristics and structural capacity are not as easily forecasted. The accuracy of pavement condition data is important, as it impacts funding needs. These metrics will be used in developing short-range CIPs when performance issues are known. For multiple airport systems, and large-, medium-, and small-hub airports, PCI data from a manual inspection at less than, at, or above a 95 percent confidence level is appropriate. In addition, non-PCI pavement condition rating system data from an aerial survey (with appropriate authoriza- tions), 3D-laser imaging, or LiDAR may also be used. It is expected that any of these data collection methods enable a characterization of the general condition of the pavement and that predicted future conditions from data generated with these technologies will be reasonably accurate. Figure 36 does not include recommendations for GA airports. Many GA airports are covered by statewide systems, which are addressed elsewhere. For the independent GA air- port that does develop long-term CIPs, budgets, or master plans, they should use Figure 36

Figure 36. Management pavement data collection decision tree.

Data Collection Guidelines 81 as a statewide system. GA airports that only develop short to mid-range CIPs or budgets are functioning more like an engineering department, which is covered in the following use category and Figure 40. Use by Engineering or Other Technical Departments Engineering and other technical departments have diverse data uses. The primary uses are shown in Figure 37, broken out by appropriate factors. Some agencies will want to continuously use pavement data in many applications, while others will limit the use of pavement data to one or two applications intermittently. APMS (Simple) If a simple APMS is implemented, the goal is to gather, store, and reference general pavement condition information. In this use the airport does not need to know the location and condition of every distress across a pavement network. The decision tree for simple and detailed APMS is shown in Figure 38. For all airports, pavement condition data collection by an aerial non-PCI pavement condition rating system and a manual PCI inspection at less than a 95 percent confidence level may be used (again, provided the proper permissions are obtained to operate an sUAS). These methods will document the condition of pavement and allow monitoring of changes over time. For multiple airport systems, statewide systems, and large-, medium-, and small-hub airports, a manual PCI inspection at or above a 95 percent confidence level may also be used with the intention of inspecting a greater area and achieving greater accuracy in results. For a small-hub or GA airport, the pavement condition may also be captured from a non-PCI visual assessment [such as a PASER survey (FAA AC 150/5320-17A) or a non-ASTM D5340 compliant survey with one of the many other available techniques]. It is recommended that standards and perhaps a data quality management plan be applied for this type of visual assessment to promote consistency. APMS (Detailed) If a detailed APMS is implemented, the goals are to gather, store, and reference general pave- ment condition information and to cross-reference these data with structural condition data and friction data. A detailed APMS will present sufficient metrics so that the pavement can be viewed as a whole. The structural condition data for all airports may be collected at the network level, while multiple airport systems, statewide systems, and large- and medium-hub airports may elect to collect at the project level. Friction condition data will be collected by CFME. For all airports, pavement condition data collection by a manual PCI inspection at less than, at, or above the 95 percent confidence level may be used. Pavement condition may also be col- lected at a 100 percent density. The density of PCI inspections should be chosen based on the quantity of pavement that the agency is comfortable inspecting and the resources available to collect the data. For multiple airport systems, statewide systems, and large- and medium-hub airports, the agency may elect to gather pavement condition data from manual distress mapping at 100 percent density, 3D-laser imaging, or LiDAR. These data are able to provide exact distress locations across the entire network. Maintenance Planning Pavement data can be used to identify which pavements are in need of maintenance. The goal of this use is to identify which areas of pavement should be examined more frequently by maintenance, operations, and engineering personnel. These areas of pavement will receive regular maintenance by in-house personnel or on-call contractors. The result of this use is not to determine specific repair projects. The decision tree for maintenance planning and determining maintenance budget/staffing is presented in Figure 39.

Figure 37. Uses by engineering or other technical departments.

Figure 38. APMS decision trees.

Figure 39. Maintenance decision trees.

Data Collection Guidelines 85 For all airports, maintenance planning can be performed from pavement condition data gathered by manual PCI inspection at less than a 95 percent confidence interval. For small-hub and GA airports, pavement condition data may be collected by a non-PCI visual assessment. For multiple airport systems, statewide systems, and large- and medium-hub airports, gathering pavement condition data by manual PCI inspection at or above a 95 percent confidence level is also an option. This use does not require data with the highest level of detail as only knowledge of the general conditions of each pavement section is needed. In addition, if longitudinal profile issues are noted by airport personnel or pilots, longitudinal profile testing should occur. The data can be gathered either from an inertial profiler or rod and level. Data from longitudinal profiling are considered a secondary input for maintenance plan- ning, as it is very uncommon for roughness issues to dictate maintenance needs (or pavement rehabilitation, for that matter). Maintenance Budget/Staffing Pavement data can also be used to create detailed maintenance budgets and project staffing needs. To successfully achieve these goals, accurate repair quantities help to estimate the level of effort needed for maintenance activities. Unlike other data uses, the factor most representative of which data collection methods are used is the primary surface type. For concrete surfaces, a visual representation of the pavement is needed in order to develop accurate repair quantities because within the PCI procedure the extent of a distress within a slab is not captured. If the characteristics of distresses within a slab are unknown, sweeping assumptions about repair quantities will be inaccurate. To develop a maintenance budget or staffing needs for concrete pavement repairs, data may be collected by manual distress mapping with 100 percent coverage or non-PCI condition rating system by aerial survey (with appro- priate permissions to operate such equipment), 3D-laser imaging, or LiDAR. These methods capture the exact location and characteristics of concrete distresses and allow detailed planning. For asphalt surfaces, the same methods as described for concrete pavements may be used. In addition, either a manual PCI inspection at 100 percent sampling or manual PCI inspection at or above a 95 percent confidence level is appropriate. PCI procedures for asphalt surfaces yield distress quantities (an area or linear measurement) that can be converted to a repair quantity with acceptable accuracy for detailed planning. CIP Development Engineering departments may develop a CIP that spans from a single year to more than 5 years. Pavement condition data are a key component in developing a CIP, although other factors not related to pavement conditions will also be considered when developing a CIP. The decision tree for CIP development is shown in Figure 40. The pavement condition data used in CIP development need to capture the general condition of the pavements. Analysis of the data leads to projections of future pavement conditions. For all airports, data collection by a manual PCI inspection at less than, at, or above the 95 percent confi- dence level may be used. An aerial non-PCI pavement condition rating system may also be used if the resulting data can be used to project future pavement condition (with appropriate permissions to operate such equipment). For multiple airport systems and large- and medium-hub airports, other pavement condition data collection methods are available. A PCI inspection at 100 percent sampling or distress mapping with 100 percent sampling can be performed. In addition, pavement condition gathered using 3D-laser imaging or LiDAR by may also be evaluated. In the development of a CIP, the structural condition data for all airports may be collected at the network level, while multiple airport systems and large- and medium-hub airports may elect

Figure 40. CIP development decision tree.

Data Collection Guidelines 87 to collect at the project level. These data will represent the current structural conditions, and short-term projections can be made about pavement’s future structural capacity. There are also various secondary data inputs that may be used in CIP development. The longitudinal profile and friction data may be considered at all airports. At multiple airport systems and large- and medium-hub airports, other measures, such as runway groove measure- ments and the macrotexture, may also be considered. All secondary inputs should be considered after the pavement condition and structural capacity. Internal Agency Funding Justification Engineering departments will sometimes be tasked with providing justification of a funding request to airport management. To accomplish this, the proper data and data analysis must be provided. The decision tree for internal agency funding justification is shown in Figure 41. For all airports, data collection by a manual PCI inspection at or above the 95 percent con- fidence level may be used. An aerial non-PCI pavement condition rating system may also be used (provided the required permissions are obtained to use the equipment). For multiple airport systems and large-, medium-, and small-hub airports, other pavement condition data col- lection methods may be used. A PCI inspection at 100 percent sampling or distress mapping with 100 percent sampling can be performed. In addition, pavement condition gathered using 3D laser imaging or LiDAR may also be evaluated. For statewide systems and GA airports, data collection by a manual PCI inspection at less than a 95 percent confidence level may be used. Structural condition data may be used for statewide systems and GA airports and are needed for other airports to justify funding internally. Airports with significant commercial traffic need to ensure either the structural capacity is adequate for a repair project or is insufficient for pave- ment requiring rehabilitation. The data may be collected at the network level for all airports. For multiple airport systems and large-, medium-, and small-hub airports, these data may be collected at the project level. There are also various secondary data inputs that may be used in internal funding justifica- tions. The longitudinal profile, macrotexture, groove measurements, and friction data may be considered at all airports. All secondary inputs should be considered after the pavement condi- tion and structural capacity. Unless clear safety issues are present, these metrics independently will not justify funding, but can support funding requests when combined with other data. Project-Level Evaluation Details of project-level evaluations are covered in Chapter 4. The scope of a project-level evaluation can be very specific or broad and use many different metrics. All data collection occurs as needed by the project. The collected data must be detailed enough to make the cor- rect long-term decision. The decision tree for project-level evaluation is presented in Figure 42 and shows that all data collection efforts may be appropriate for any category of airport. Other Uses PCN Reporting to Other Users Most airport users are not concerned with the details of pavement condition data. One excep- tion is that some users (e.g., airlines and certain private owners/operators) need to know the PCN of certain pavements to ensure their aircraft do not cause damage. This is especially a concern when the aircraft in question is larger than the typical aircraft at that airport. Another exception is that pilots may want to know the skid number prior to landing, although this is a weather-related concern and not a part of managing pavement condition data.

Figure 41. Internal agency funding justification decision tree.

Figure 42. Project-level evaluation decision tree (GPR = ground penetrating radar).

90 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports In Figure 43 potential data collection methods and frequency are shown for use by others. To gather the required information about the strength of the pavement, FWD/HWD testing at the network level is required. More detailed data collection is not needed to capture general information of the pavement capacity. Condition Data Cost and Value Guidance has been presented on a range of pavement condition data collection methods based on variables such as the use of the data and the airport size. In many cases this guidance identifies different techniques for collecting what is essentially the same data. In addition to different technologies associated with the different data collection methods, other factors may vary, such as the data collection time, the conditions under which the methodology can be used, and the impact on operations. One additional variable that has not yet been discussed is the cost of collecting different types of condition data. An associated consideration regarding costs is whether different data collection methods provide different value. As an example, once access to different technologies is resolved, the costs of performing a pavement condition survey will vary based on the sampling rate and access constraints, with higher costs associated with a survey of one hundred percent of the pavement and limited access or nighttime data collection. In some cases, it may be less expensive to collect similar condition data with digital video technology or with a vehicle equipped with automated data collection equipment, but the costs could be higher to manually extract distress data. An associated issue arises in determining the value of the collected data. If a condition data collection method does not provide information on every PCI distress, it, by definition, has less value than another method that includes every PCI distress. However, if an airport can make acceptable decisions Figure 43. Other uses decision tree.

Data Collection Guidelines 91 for maintenance, CIP development, planning, and other purposes with this lesser dataset, then it has sufficient value and should be considered. As is implied in this simple explanation, the value of one type of data over another, or one data collection method over another, is complicated to quantify. If a set of data leads to a better maintenance, engineering, planning, capital project, or operational decision, it has greater value than the dataset that makes a lesser contribution. Determining that value remains a challenge. However, the costs are easy to quantify, especially if data collection is done by a contractor. The costs of different approaches can be obtained by allowing alternate bids or requesting contractors to bid alternative methodologies. With the costs in hand, an airport or agency may determine the value of different types and quality of data and decide which approaches are most cost effective.

Next: Chapter 8 - Next Generation Pavement Condition Data »
Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Get This Book
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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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