A Streamlined Bridge Inspection Framework Utilizing Unmanned Aerial Vehicles (UAVs)

Recently, the rapid development of commercial unmanned aerial vehicles (UAVs) has made collecting images of bridge conditions trivial. Measuring a defect’s extent, growth, and location from the collected big image set, however, can be cumbersome. This paper proposes a streamlined bridge inspection system that offers advanced data analytics tools to automatically: (1) identify type, extent, growth, and 3-D location of defects using computer vision techniques; (2) generate a 3-D point cloud model and segment structural elements using human-in-the-loop machine learning; and (3) establish a georeferenced elementwise as-built bridge information model to document and visualize damage information. This system allows bridge managers to better leverage UAV technologies in bridge inspection and conveniently monitor the health of a bridge through quantifying and visualizing the progression of damage for each structural element. The efficacy of the system is demonstrated using two bridges.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1052
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/60736
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Colorado State University, Fort Collins

  Department of Civil and Environmental Engineering
  Fort Collins, CO  United States  80525

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Guo, Yanlin
  • Perry, Brandon J
  • Atadero, Rebecca
  • van de Lindt, John W
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 59p

Subject/Index Terms

• TRT Terms: Bridges; Drones; Inspection; Machine learning; Structural health monitoring
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01832401
• Record Type: Publication
• Report/Paper Numbers: MPC 21-443, MPC-535, MPC-592
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jan 13 2022 1:52PM