AI Applications for Automatic Pavement Condition Evaluation (2024)
Chapter: Appendix A: Agency Survey Questionnaire
APPENDIX A
Agency Survey Questionnaire
NCHRP TOPIC 54-14
ARTIFICIAL INTELLIGENCE APPLICATIONS FOR AUTOMATIC
PAVEMENT CONDITION EVALUATION
QUESTIONNAIRE
The following includes the proposed questions included in the agency questionnaire. The proposed questions included yes/no responses or asked the user to select from a specific list of responses and provided space for users to include comments as needed.
Dear Agency Pavement Management Representative,
The Transportation Research Board (TRB), through the National Cooperative Highway Research Program (NCHRP), under the sponsorship of the American Association of State Highway and Transportation Officials (AASHTO), and in cooperation with the Federal Highway Administration (FHWA) is preparing a synthesis report on artificial intelligence applications for automatic pavement condition evaluation.
The purpose of this questionnaire is to identify and summarize the procedures and practices used by state DOTs related to the use of artificial intelligence technology with a fully automated pavement condition survey. The results of the questionnaire will be incorporated into a synthesis of DOT practice, with the intent of helping agencies evaluate and improve their current practices.
This questionnaire is being sent to personnel responsible for pavement management at all state DOTs. If you are not the appropriate person at your agency to complete this questionnaire, please forward this request to the correct person. A PDF of the questionnaire is attached so you may preview all of the questions.
Please complete and submit this questionnaire by July 21, 2023. We estimate that it should take no more than 30 minutes to complete. If you have any questions or problems with operation or access to the questionnaire, please contact our principal investigator Dr. Linda Pierce.
QUESTIONNAIRE TIPS
If you are unable to complete the questionnaire, you can return to the questionnaire at any time by reentering through the questionnaire link as long as you access the questionnaire through the same computer. Re-entering the questionnaire will return you to the last completed question.
Questionnaire navigation is conducted by selecting the “prev” (previous) or “next” button at the bottom of each page.
Thank you for your time and expertise in completing this important questionnaire.
ACRONYMS
AASHTO – American Association of State Highway and Transportation Officials
AI – artificial intelligence
DOT – Department of Transportation
GIS – Geographic information system
HPMS – Highway Performance Monitoring System
IRI – International Roughness Index
LLM – large language models
MAP-21 – Moving Ahead for Progress in the 21st Century Act
PMS – pavement management system
DEFINITIONS
Agency – for data collection and analysis of the automated pavement condition survey, the use of “agency” implies agency data collection and analysis, vendor data collection and analysis, or in combination.
Artificial intelligence – computer-based methodologies for identifying pavement distress types and distress severity and extent.
Automated pavement condition survey – fully-automated methods (i.e., minimal to no user interaction) for detecting surface distress, excludes the collection and analysis of inertial profile data (e.g., IRI, faulting, rutting).
QUESTIONS
Name_______________________________________________
Organization _________________________________________
E-mail Address _______________________________________
Phone Number _______________________________________
GENERAL
- Does your agency conduct an automated pavement condition survey to quantify pavement surface distress (excludes inertial profile measurements)?
| □ Yes | □ No |
- Does your agency use AI technology to process the automated pavement condition survey?
| □ Yes | □ No |
| □ Not sure |
- Does your agency use other technology to complement the automated pavement condition survey (e.g., smartphones to track potholes, more frequent assessment of safety-related distress)?
| □ Yes | □ No |
| □ Not sure |
- Do you think the new technology (e.g., autonomous vehicle, crowdsource) may be used for future AI-based automated pavement condition data collection?
| □ Yes | □ No |
| □ Not sure |
- Would your agency consider using AI in the future to collect and process the automated pavement condition data?
| □ Yes | □ No |
| □ Not sure |
AUTOMATED PAVEMENT CONDITION SURVEYS
- Is the automated pavement condition survey conducted using agency equipment and staff or contracted through a vendor?
| □ Conducted by agency | □ Conducted by vendor |
| □ Conducted by agency and vendor | □ Other (please specify) |
- Please indicate the vendor or equipment provider your agency uses for the automated pavement condition survey (the vendor or equipment provider name will not be provided in the synthesis document).
| □ Vendor A | □ Vendor B |
| □ Vendor C | □ Vendor D |
| □ Vendor E | □ Other (please specify) |
- Requirements for automated pavement distress identification include (select all that apply):
| □ Distress type | □ Distress severity |
| □ Distress extent | □ Precision and accuracy |
| □ Data repeatability | □ Sampling rate |
| □ Linear referencing system | □ Spatial resolution |
| □ Reporting interval | □ Productivity (miles/day) |
| □ Compatibility with existing PMS | □ Other (please specify) |
- What data format requirements are needed for AI training/post-processing?
| □ No additional requirements | □ Not sure |
| □ Other (please describe) |
- What image quality requirements are needed to conduct AI training/post-processing?
| □ No additional requirements | □ Not sure |
| □ Other (please describe) |
AUTOMATED PAVEMENT CONDITION SURVEY RESULTS
- Pavement surface condition data are used for (select all that apply):
| □ Pavement performance modeling | □ Road safety assessment |
| □ Pavement condition rating or index | □ Detecting prevalent distress type |
| □ Detecting individual distress types | □ HPMS reporting |
| □ MAP-21 reporting | □ Other (please specify) |
- Pavement condition data is used to support the following agency decision-making activities (select all that apply):
| □ Budgeting | □ Multi-year budget planning (network) |
| □ Approximate bid quantities (project) | □ Trigger safety-related repairs |
| □ Treatment selection | □ Targeted performance goals |
| □ Establish performance targets | □ Verify performance models |
| □ Contract performance specifications and measures | □ Other (please specify) |
ARTIFICIAL INTELLIGENCE
- What asphalt-surfaced pavement condition types does your agency evaluate using AI technology (select all that apply)?
| □ Alligator cracking | □ Bleeding |
| □ Block cracking | □ Delamination/potholes |
| □ Edge cracking | □ Longitudinal cracking |
| □ Patching | □ Raveling |
| □ Potholing | □ Transverse cracking |
| □ Weathering | □ Not sure |
| □ Other (please specify) |
- What concrete-surfaced pavement condition types does your agency evaluate using AI technology (select all that apply)?
| □ Not applicable (pavement type not used) | □ Blowups |
| □ Corner cracking | □ Joint seal damage |
| □ Longitudinal cracking | □ Map cracking |
| □ Patching | □ Polished aggregate |
| □ Pumping | □ Punchout |
| □ Scaling | □ Spalling |
| □ Transverse cracking | □ Not sure |
| □ Other (please specify) |
- What other roadway features does your agency assess using AI technology (select all that apply)?
| □ Not applicable | □ Right-of-way (e.g., slope, embankment) |
| □ Excess vegetation growth | □ Roadside assets (e.g., markings, signs) |
| □ Other (please specify) |
- What AI technologies, tools, and models does your agency currently use for pavement condition evaluation (select all that apply)?
| □ Machine learning | □ Pattern recognition (e.g., data mining) |
| □ Neural network | □ Deep learning |
| □ Random forest | □ Not sure |
| □ Other (please specify) |
- How does your agency conduct AI-technique development, training, and evaluation (e.g., ground truth testing) (select all that apply)?
| □ Pre-defined reference sections | □ Random reference sections |
| □ Compare to manual surveys | □ Compare to traditional automated pavement condition survey |
| □ Accuracy, precision, and repeatability | □ Google Earth images for AI training |
| □ Not sure | □ Other (please specify) |
- Is your agency’s AI process in accordance with AASHTO R 85-18?
| □ Yes | □ No |
| □ Not sure | □ Other (please specify) |
- Can your agency’s AI process be used on historical records (i.e., archived videos or images)?
| □ Yes | □ No |
| □ Not sure | □ Other (please specify) |
- What challenges does your agency have with the current AI process for automated pavement condition surveys?
| □ Limited agency knowledge | □ Computer computation capabilities |
| □ AI training | □ Ground truth testing |
| □ Trusting results | □ Other (please specify) |
- What are the agency benefits of using AI for processing the automated pavement condition survey?
| □ Increased productivity | □ Objectivity (consistent assessment) |
| □ Accuracy (once trained) | □ Cost |
| □ Other (please specify) |
IN CLOSING
- Do you have any other suggestions or comments related to using AI with automated pavement condition surveys?
| □ Yes (please specify) | □ No |
- Are you willing to participate in a follow-up interview (via email) in the event additional information or clarification of your responses are needed?
| □ Yes | □ No |
- The synthesis will also include case examples highlighting agency practices related to AI technology and automated pavement condition surveys. Agencies will be provided the opportunity to review the case example write-up for accuracy. Would your agency be interested in participating in a case example?
| □ Yes | □ No |
- If available, please include additional documentation related to AI and automated pavement condition surveys.
| □ Yes (can provide a file) | □ Yes (can provide a link) |
| □ No |
