SUMMARY

AI Applications for Automatic Pavement Condition Evaluation

Departments of transportation (DOTs) use pavement condition surveys to assess current pavement conditions and predict future pavement conditions to (1) determine the type and timing of maintenance and rehabilitation treatments; (2) define future budget needs; and (3) develop, or refine, pavement performance models. Over the last two decades, pavement condition assessment has transitioned from manual to semiautomated to fully automated pavement condition surveys (APCSs). Due to large volumes of data and images and advances in computing and processing, the utilization of artificial intelligence (AI) technology for distress identification has increased in recent years. While AI technologies continue to evolve, they have demonstrated efficiency and accuracy (when trained and validated) in identifying and quantifying pavement distress (e.g., cracking, rutting, and potholing).

The objective of this synthesis is to document state DOT automated pavement distress identification and the use of AI [i.e., machine learning/deep learning (ML/DL)] technologies to evaluate pavement conditions.

This synthesis is based on the combined results of a literature review of AI technology for an APCS, a survey of state DOTs, and follow-up questions with DOTs that indicated having experience using AI to analyze an APCS.

Forty-three (43) of the 52 surveyed DOTs (83%) responded to the survey. Of the 43 responding DOTs, 38 (88%) indicated using an APCS to determine pavement condition. APCS data and image collection requirements included:

• Distress type and severity (38 of 43 DOTs or 88%),
• A linear referencing system (37 of 43 DOTs or 86%),
• Distress extent (35 of 43 DOTs or 81%),
• Reporting interval (31 of 43 DOTs or 72%),
• Data repeatability (30 of 43 DOTs or 70%),
• Precision and accuracy (29 of 43 DOTs or 67%),
• Compatibility with the existing pavement management system (24 of 43 DOTs or 56%),
• Spatial resolution (19 of 43 DOTs or 44%),
• Sampling rate (15 of 43 DOTs or 35%), and
• Productivity (11 of 43 DOTs or 26%).

The predominant distress types identified for asphalt pavements included transverse and longitudinal cracking (7 of 43 DOTs or 16%) and alligator cracking (5 of 43 DOTs or 12%); the predominant distress types identified for concrete pavements included transverse (6 of 43 DOTs or 14%) and longitudinal cracking (5 of 43 DOTs or 12%).

Eighteen (18) of 43 DOTs (42%) indicated AI technology was not used to analyze the APCS results, 12 of 43 DOTs (28%) indicated being unsure, and 8 of 43 DOTs (19%)

indicated AI technology was used to analyze the APCS results. AI models used for distress detection included:

• Random forest (RF) (2 of 43 DOTs or 5%),
• ML (2 of 43 DOTs or 5%),
• Neural networks (1 of 43 DOTs or 2%),
• Pattern recognition (1 of 43 DOTs or 2%), and
• Unsure which AI models were used (16 of 43 DOTs or 37%).

AI model selection (or development), training, and validation are needed to improve and validate the analysis results. Most agencies (15 of 43 DOTs or 35%) were unsure how the AI results were developed, trained, or evaluated. Conversely, 6 of 43 DOTs (14%) noted using accuracy, precision, and repeatability criteria; 5 of 43 DOTs (12%) compared the AI results to manual survey results; 3 of 43 DOTs (7%) each compared random or predefined reference sections to the AI results; and 2 of 43 DOTs (5%) compared the AI results to traditional APCS results.

APCS results are predominantly used for Highway Performance Monitoring System (HPMS) (38 of 43 DOTs or 88%) and MAP-21 reporting (33 of 43 DOTs or 77%), pavement condition assessment and performance modeling (36 of 43 DOTs or 84% for both) and prevalent distress type detection (22 of 43 DOTs or 51%). Regarding agency decision-making efforts, the APCS results are used to verify performance models (32 of 43 DOTs or 74%), establish performance targets (31 of 43 DOTs or 72%), target performance goals (31 of 43 DOTs or 72%), select treatment types (31 of 43 DOTs or 72%), and plan multiyear budgets (31 of 43 DOTs or 72%).

Agencies also noted several benefits of using AI technology, including objectivity (14 of 43 DOTs or 33%), accuracy and increased productivity (13 of 43 DOTs or 30% for both), and cost savings (7 of 43 DOTs or 16%). Conversely, agencies noted challenges of using AI technology, included accuracy, precision, and repeatability (6 of 43 DOTs or 14%); comparison to manual surveys (3 of 43 DOTs or 7%); use of random and predefined reference sections (3 of 43 DOTs or 7% for both); and comparison to traditional APCS results (2 of 43 DOTs or 5%).

Several future research needs were identified to expand the use and understanding of AI technology to analyze APCS results. Future research needs include:

• Developing a standard image format (e.g., .jpeg, .png, private extension) and distress library (e.g., pavement type, distress types, severity levels);
• Developing a publicly available image data set for AI model training;
• Providing guidance for AI model selection (or development), recalibration, training, and validation; and
• Developing a methodology for using AI technologies in the quality control and acceptance process.