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

Details

• Creators:
  Guo, Yanlin ; Perry, Brandon J ; Atadero, Rebecca ; van de Lindt, John W.
• Corporate Creators:
  Colorado State University. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Mountain-Plains Consortium ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Bridges
  • Drones
  • Inspection
  • Machine learning
  • Structural health monitoring
  • Data collection
  • Feasibility analysis
  • Guidelines
  • Mathematical models
  • Remote sensing
• Publication/ Report Number:
  MPC-535, MPC-592 (combined)
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Colorado
• Edition:
  Final Report
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  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.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/60736/dot_60736_DS1.pdf
• File Type:
  [PDF - 2.31 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:aca274a485f1469f8f48a660b099deca2f1611450e9f9d225fd97ba29ab2bdd4617c3538dd66e41dd476de350304c2f546ca5b15314f11a543321366ab033ceb