Automated Assessment of Defects in Bridge Structures

Details

• Creators:
  Shafei, Behrouz ; Odeh, Ibrahim
• Corporate Creators:
  Iowa State University. Bridge Engineering Center
• Corporate Contributors:
  Iowa State University. Institute for Transportation (InTrans). ; Iowa. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Artificial intelligence
  • Bridge management systems
  • Inspection
  • Condition surveys
  • Structural deterioration and defects
  • Neural networks
  • Drones
  • Machine learning
• Publication/ Report Number:
  InTrans Project 22-798
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Iowa State University. Institute for Transportation (InTrans).
• Abstract:
  Effective and timely bridge inspections are crucial for extending bridge lifespans and preventing catastrophic failures. Traditional inspection methods often involve manual visual assessments and can be time-consuming, labor-intensive, and prone to human error. Recent technological advancements in unmanned aerial vehicles (UAVs), artificial intelligence (AI), and machine learning (ML) offer promising solutions to these challenges. When high-quality images captured by UAVs are analyzed using AI and ML algorithms, structural defects can be detected and quantified with greater precision and efficiency than manual inspections.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/85148/dot_85148_DS1.pdf
• File Type:
  [PDF - 10.98 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:54f5c2d55e19c3d25f5c2dae5304693bfc119945d483f0db07f82c3a8c2baab0a8951ee93be5f8a070a592d280d775ed82bf85013e8c8df46b137cd1cd80d7d9