Agentic AI for ASTM-Aligned Pavement Assessment Framework

This project proposes an agentic artificial intelligence (AI) framework for pavement assessment that brings together multimodal deep learning, depth-aware evidence, and standards-aligned reasoning within a single workflow. Although recent automated methods have improved distress detection, segmentation, and severity labeling, they still function largely as isolated tasks and therefore do not support the broader engineering reasoning required for maintenance decision-making. As a result, an important gap remains between model prediction and practical pavement assessment. Building on prior advances in pavement condition index estimation, dense captioning, and annotation-efficient segmentation, the proposed framework will detect and classify pavement distress, estimate severity and depth-related condition indicators, identify the appropriate ASTM-aligned assessment pathway, and generate clear engineering-oriented reports. In this way, the project connects perception, interpretation, pathway selection, and reporting in a coordinated system. Ultimately, it aims to move pavement AI beyond passive prediction toward a more interpretable, scalable, and decision-ready tool for transportation practice.

• Record URL:
  • https://www.ctips.org/projects/details.php?id=672

Language

• English

Project

• Status: Active
• Funding: $125,089.00
• Contract Numbers:

  69A3552348308

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Transformative Infrastructure Preservation and Sustainability

  North Dakota State University
  Fargo, North Dakota  United States  58108-6050
• Performing Organizations:

  North Dakota State University

  Civil and Environmental Engineering
  Fargo, ND  United States 
• Principal Investigators:

  Aboah, Armstrong

• Start Date: 20260828
• Expected Completion Date: 20291130
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: CTIPS-074

Subject/Index Terms

• TRT Terms: Artificial intelligence; Detection and identification technologies; Pavement distress; Pavement management systems
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 02002023
• Record Type: Research project
• Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
• Contract Numbers: 69A3552348308
• Files: UTC, RIP
• Created Date: Sep 14 2026 2:03PM