AI-Powered Bridge Defect Detection and Condition Assessment Using Drone-Collected Visual Data

Details

• Creators:
  Tsegaye, Natnael Taye ; Cui, Qingbin ; Zhang, Yunfeng
• Corporate Creators:
  University of Maryland (College Park, Md.)
• Corporate Publisher:
  Springer Nature
• Corporate Contributors:
  Strengthening Mobility and Revolutionizing Transportation (SMART) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; North Central Regional Planning Commission
• Subject/TRT Terms:
  • Datasets
  • Bridges
  • Structural health monitoring
  • Concrete
  • Drones
  • Unmanned aircraft systems
  • Artificial intelligence
• DOI:
  https://doi.org/10.1038/s44172-026-00749-7
• Resource Type:
  Journal Article
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Description:
  Maintaining safe bridges requires regular inspections, but traditional methods depend on manual visual surveys that are labor-intensive, subjective, and often limited by access and safety constraints. Recent advances in drones and artificial intelligence (AI) provide opportunities to improve efficiency, consistency, and coverage in bridge monitoring. In this study, we demonstrate a workflow that uses drone-collected data to detect and segment eight types of concrete deterioration. Post-processing methods stabilize detections across video frames and produce machine-readable outputs that align closely with manual tallies. Results from ten bridges in Kansas indicate that the automated defect counts exhibit strong directional consistency with the nationally reported condition ratings. The approach also projects detections into three-dimensional reconstructions for measurement and visualization, and a prototype web service enables near-real-time deployment. Collectively, these findings suggest that drone- and AI-powered inspections can effectively complement traditional inspection practices while offering scalable and data-driven support for infrastructure management.
• Content Notes:
  This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation: Tsegaye, N.T., Cui, Q. & Zhang, Y. AI-powered bridge defect detection and condition assessment using drone-collected visual data. Commun Eng (2026). https://doi.org/10.1038/s44172-026-00749-7
• Format:
  PDF
• Funding:
  69A3552341015 SMARTFY22N1P1G24
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/94259/dot_94259_DS1.pdf
• File Type:
  [PDF - 2.79 MB]
• Collection(s):
  SMART Program
• Main Document Checksum:
  urn:sha-512:a7ce2d4137e9d341db015b5419e574c6f01d667a75c171dbed0062a3a1a83e13e7a36d9d35d286e4e15d0a60d2d81f9e7814403c8776db22b4f49bdd94252416