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A-1 Decision Tree Example A P P E N D I X A Deciding what pavement condition data to collect and how to collect it is a complex matter, as it is in part determined by the need to comply with FAA regulations and in part a function of specific airport or agency needs and practices. To aid in selecting which data collection methods should be used, several decision trees are presented in Chapter 7. Each decision tree is for a category of data use: ⢠Data use for FAA compliance. ⢠Data use by airport or agency management. ⢠Data use by engineering or other technical departments. ⢠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 evaluated if the agency is not concerned with that specific data use. To use the decision tress, 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-2 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports occurrences of each data collection method from step 3. At this point the agency would evaluate the various data collection methods by performing step 4. Based on the results in Table A-2, the agency would examine the results for each category of data collection type (e.g., pavement condition, structural condition, and surface characteristics). In the example below, the agency first would determine if its pavement condition needs are met with either a manual PCI inspection at less than 95 percent confidence level or a manual PCI inspection at or above a 95 percent confidence level. Both these methods do meet the desired data uses. If these inspection methods do not meet all of the requirements, a determination of which other methods could supplement data collection would need to be made and how the multiple methods of pavement condition data would interact. Then the agency would identify any additional factors that would be advantages or disadvantages to specific data collection methods, such as how limited access to a runway would impact a manual PCI inspection. Lastly, if the identified factors would not hamper the effectiveness of a manual PCI inspection, the costs of the data collection effort for both inspection densities should be calculated and value of the condition data to the agency should be estimated. The agency can then perform a cost-benefit analysis to select the most effective data collection method. Likewise, the data collection methods for structural condition and surface characteristics would also be examined in the same manner. An example is provided for a large-hub airport where there are nine potential data uses for the airport related to FAA compliance, management decisions, and engineering or other technical departments. Table A-1 presents the results from steps 1 and 2. Table A-2 is a summary of the
Table A-1. Use of decision trees to select possible data collection methods. General Data Purpose Specific Data Purpose Data Collection Type Data Collection Methods FAA Compliance Runway Friction Data Reporting Surface Characteristics CFME friction data collection Runway PCN Data Reporting Structural Condition FWD/HWD collection at project level FWD/HWD collection at network level Pavement Condition Data Reporting Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level PCI manual inspection at 100% sampling PCI manual inspection at 100% with distress mapping Non-PCI pavement condition rating system by aerial survey Non-PCI pavement condition rating system by 3D laser imaging Non-PCI pavement condition rating system by LiDAR Management Long-Term Agency CIP/Budgeting or Updating Master Plan Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level Non-PCI pavement condition rating system by aerial survey Non-PCI pavement condition rating system by 3D laser imaging Non-PCI pavement condition rating system by LiDAR External Funding Justification Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level Structural Condition FWD/HWD collection at network level - optional Other PCN Reporting to Users Structural Condition FWD/HWD collection at network level Engineering/Other Technical Departments Maintenance Planning (Identifying pavement in need of repairs) Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level Surface Characteristics Longitudinal profile (inertial profiler) - secondary data input Longitudinal profile (rod and level) - secondary data input
Table A-1. Use of decision trees to select possible data collection methods (continued). General Data Purpose Specific Data Purpose Data Collection Type Data Collection Methods Engineering/Other Technical Departments APMS (Detailed) Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level PCI manual inspection at 100% sampling PCI manual inspection at 100% with distress mapping Non-PCI pavement condition rating system by 3D laser imaging Non-PCI pavement condition rating system by LiDAR Structural Condition FWD/HWD collection at project level FWD/HWD collection at network level Surface Characteristics CFME friction data collection CIP Development Pavement Condition PCI manual inspection at less than 95% confidence level PCI manual inspection at or above 95% confidence level PCI manual inspection at 100% sampling PCI manual inspection at 100% with distress mapping Non-PCI pavement condition rating system by aerial survey Non-PCI pavement condition rating system by 3D laser imaging Non-PCI pavement condition rating system by LiDAR Structural Condition FWD/HWD collection at project level FWD/HWD collection at network level Surface Characteristics CFME friction data collection - secondary data input Manual runway groove measurements - secondary data input Macrotexture (sand patch) - secondary data input Macrotexture (CT Meter) - secondary data input Longitudinal profile (inertial profiler) - secondary data input Longitudinal profile (rod and level) - secondary data input
Decision Tree Example A-5 Table A-2. Summary of possible data collection methods. Data Collection Type Data Collection Methods Number of Occurrences Pavement Condition PCI manual inspection at less than 95% confidence level 6 PCI manual inspection at or above 95% confidence level 6 Non-PCI pavement condition rating system by 3D laser imaging 4 Non-PCI pavement condition rating system by LiDAR 4 Non-PCI pavement condition rating system by aerial survey 3 PCI manual inspection at 100% sampling 3 PCI manual inspection at 100% with distress mapping 3 Structural Condition FWD/HWD collection at network level 5* FWD/HWD collection at project level 3 Surface Characteristics CFME friction data collection 2 Longitudinal profile (inertial profiler) - secondary data input 2 Longitudinal profile (rod and level) - secondary data input 2 CFME friction data collection - secondary data input 1 Macrotexture (CT Meter) - secondary data input 1 Macrotexture (sand patch) - secondary data input 1 Manual runway groove measurements - secondary data input 1 *4 primary occurrences +1 optional data input
