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ACRP 09-17

ACRP 09-17 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports. Presentation Template Day, Month, Year. Project Team. David Peshkin (PI), Peter-Paul Dzwilewski, Kyle Potvin, Katherine Gauthier, Monty Wade – Applied Pavement Technology, Inc. Co-Authors :

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ACRP 09-17

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  1. ACRP 09-17 Guidelines for Collecting, Applying, and Maintaining Pavement Condition Data at Airports Presentation Template Day, Month, Year

  2. Project Team • David Peshkin (PI), Peter-Paul Dzwilewski, Kyle Potvin, Katherine Gauthier, Monty Wade – Applied Pavement Technology, Inc. • Co-Authors: • Woolpert – Eric Risner, Ryan Robinson, Chris Snyder, Marianne Cardwell • PMS Ltd. (Ireland) – Kieran Feighan

  3. ACRP Report 09-17Project Panel • Casey Ries, Gerald R. Ford International Airport (Panel Chair) • Alexander K. Bernier, Stantec Consulting Services • Angela Newland, CCI Engineering Services • Owen Silbaugh, Massachusetts DOT • Dianne Walker, DW LLC • Kelvin Wang, Oklahoma State University • Gregory Cline, Al Larkin – FAA Liaisons • Christine Gerencher, TRB Liaison • Theresia Schatz, ACRP Senior Program Officer

  4. Presentation Overview • Project background • Objective • Types of pavement condition data • Uses of pavement condition data • Matching condition data types and use(s) • Data storage, maintenance, and access

  5. PROJECT BACKGROUND: THE CHALLENGE • Collecting pavement condition data can be expensive and time-consuming • Access to collect such data is increasingly challenging • Standards have not kept pace with new data collection technologies • Little guidance available on need for and uses of many types of condition data

  6. PROJECT OBJECTIVE • Best practice guidelines for airport pavement data • Collection • Use • Maintenance and storage

  7. Study Approach • Review literature • Survey airport practices • Survey consulting engineering practices • Develop case studies • Create Guidelines

  8. Case Study Airports • Houston Airport System (Houston, Texas) • Salt Lake City Department of Airports (Salt Lake City, Utah) • Dublin International (Dublin, Ireland) • Columbus Regional Airport Authority (Columbus, Ohio) • Gerald R. Ford International Airport Authority (Grand Rapids, Michigan) • North Dakota (statewide) • Missouri (statewide)

  9. TYPES OF PAVEMENT CONDITION DATA • Distress • Surface characteristics • Structural condition

  10. Distress Condition Data

  11. Distress Data Collection • Visual (manual) condition survey • Light Detection and Ranging (LiDAR) • Two- and three-dimensional (2D and 3D) laser imaging • Vehicle-mounted camera survey • Unmanned-mounted camera survey (small unmanned aircraft system or unmanned aerial vehicle [sUAS/UAV])

  12. LiDAR Data Collection

  13. LiDAR Data Example

  14. Laser Imaging Data Collection

  15. Laser Imaging Data Example

  16. sUAS/UAV Data Example

  17. ASTM D5340 Asphalt Distress Identification (1 of 2)

  18. ASTM D5340 Asphalt Distress Identification (2 of 2)

  19. ASTM D5340 Concrete Distress Identification (1 of 2)

  20. ASTM D5340 Concrete Distress Identification (2 of 2)

  21. Surface Characteristics Overview

  22. Longitudinal Profile Data Collection • Rolling surface profilers • Rod and level • Autorod and level • Inertial profiler (ASTM E950 Class 1) • Accelerometer (smartphone application)

  23. Surface Characteristics (Friction and Grooves) • Continuous Friction Measurement Equipment (CFME) • Circular Track Texture (CT) Meter • British Pendulum • Sand patch • Manual or automated measure of groove spacing/depth

  24. Structural Condition Data Collection • Falling Weight Deflectometer (FWD) • Rolling Weight Deflectometer (RWD) and Traffic Speed Deflectometer (TSD)

  25. Structural Condition Evaluation Equipment

  26. Structural Condition Data

  27. USES OF PAVEMENT CONDITION DATA • Compliance with FAA regulations • Network-level management • Strategic-level management • Project-level assessment • Maintenance and repair plans • Troubleshooting and forensics • Communication to stakeholders

  28. Compliance with FAA Regulations • FAA AC 150/5200-18: daily, weekly, monthly, and quarterly to identify safety hazards • FAA AC 150/5380-7B: annual condition survey or triennial PCI survey • FAA AC 150/5320-12C or 12D (draft): friction measurements depending on operations • FAA AC 150/5335-5C: Pavement Classification Number (PCN) reporting

  29. Network-Level Management • High-level monitoring of overall conditions • Based on sampling rather than 100% inspection • Uses: • Document pavement performance • Set priorities • Predict future conditions and needs

  30. Strategic-Level Management • Augments network-level management • Often iterative process • Components: • Budget scenarios • Long-term planning • Stakeholder input considered

  31. Development Process (1 of 2)

  32. Development Process (2 of 2)

  33. Project-Level Assessment • Detailed evaluation of specific conditions • May be based on 100% inspection rather than sampling • Uses: • Maintenance, preservation, or rehabilitation decisions • Project design

  34. Maintenance and Repair • Maintenance • Determine needs • Condition data used to match distress types and repair quantities • Repair • Design based on distress and structural capacity

  35. Repair Plan Example

  36. Troubleshooting and Forensics • Addresses specific problem(s) • Considers several aspects of observed distresses • Used to link distresses and their causes to propose solutions • May be most detailed evaluation

  37. Communication with Stakeholders • Explain current conditions • Support project or funding requests • Impact of actions or inaction

  38. MATCHING CONDITION DATA TYPES AND USE(S) • Goal is to collect appropriate data that will be used effectively by airports • Date use controls which collection methods are implemented • Decision trees provide guidance to match condition data types and use(s)

  39. Decision Tree Categories • Organized by purpose (use), airport size, and user • Data use categories: • FAA compliance • Airport or agency management • Engineering or other technical departments • Other data uses

  40. Decision Tree Steps (1 of 2) • Decide how the data will be used • Based on the decision trees, select the possible data collection methods • Record the total occurrences for each data collection method

  41. Decision Tree – FAA Compliance

  42. Decision Tree – Management

  43. Decision Tree – Engineering (Overview)

  44. Decision Tree – Engineering (APMS)

  45. Decision Tree – Engineering (Maintenance)

  46. Decision Tree – Engineering (CIP Development)

  47. Decision Tree – Engineering (Justifying Funding)

  48. Decision Tree – Engineering (Project Level)

  49. Decision Tree – Other

  50. Decision Trees Steps (2 of 2) • Evaluate the most common available data collection methods • Do the most common data collection methods meet all of the specific uses? • Will a combination of data collection methods be required? • Identify other factors impacting data collection and use • Estimate the cost for data collection and value of associated condition data

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