A Fatigue Assessment Framework for Steel Bridges Using Fiber Optic Sensors and Machine Learning

Details

• Creators:
  Soliman, Mohamed ; Mahmoud, Ahmed ; Nayak, Aditya ; Floyd, Royce W
• Corporate Creators:
  Oklahoma State University. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Oklahoma. Department of Transportation. Office of Research and Implementation
• Subject/TRT Terms:
  • Fiber optics
  • Machine learning
  • Steel bridges
  • Structural health monitoring
• Publication/ Report Number:
  FHWA-OK-25-02
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Oklahoma
• Edition:
  Final Report
• Corporate Publisher:
  Oklahoma. Department of Transportation
• Abstract:
  This report discusses the results of a research project aiming at developing data-driven approaches for damage detection in steel bridges. The developed approaches utilize machine learning (ML) models coupled with strain data obtained from fiber optic sensors or traditional foil-type strain gauges to detect the damage. The report discusses the experimental investigations conducted throughout the project and the results of the developed damage detection approaches.
• Format:
  PDF
• Funding:
  2291, SPRY-0010(088)RS, SPRY-0010(093)RS
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86283/dot_86283_DS1.pdf
• File Type:
  [PDF - 5.32 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:739c57a02723d106a02109234b851aa1e0fca65a84a25c1c9d2d5181f9c872a848187f96dbbb7fcaf3012cffee2bbcdab177b5d395609230aa3cef745e090396