National Academies Press: OpenBook
« Previous: Glossary
Page 103
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 103
Page 104
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 104
Page 105
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 105
Page 106
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 106
Page 107
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 107
Page 108
Suggested Citation:"Appendix A - Decision Tree Example." 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.
×
Page 108

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.

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

Next: Appendix B - Case Studies »
Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

“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.
  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!