Addressing the Feasibility of Employing NDE Data for Bridge Condition Assessment Using Gaussian Process Regression [Research Brief]

Details

• Creators:
  Atadero, Rebecca ; Guo, Yanlin ; Elnahla, Mahmoud
• Corporate Creators:
  Colorado State University
• Corporate Contributors:
  Mountain-Plains Consortium ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Asset management
  • Bridges
  • Condition surveys
  • Data analysis
  • Evaluation
  • Infrastructure
  • Life cycle costing
  • Nondestructive tests
  • Freeze thaw tests
• Publication/ Report Number:
  618
• Resource Type:
  Brief
• Geographical Coverage:
  United States ; North Dakota
• Edition:
  Research Brief
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  This project tested whether limited non-destructive evaluation (NDE) data can be used to predict bridge deck deterioration more effectively. NDE tests, such as Impact Echo, provide objective measurements of deck damage, but they are costly and not collected regularly. Using bridge data from the Federal Highway Administration’s Long-Term Bridge Performance program, the research team applied a statistical learning model to estimate deck delamination. The model was trained on 20 sets of NDE records and included variables such as deck thickness, traffic levels, freeze-thaw cycles, and design details.
• Format:
  PDF
• Funding:
  MPC 25-579
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/87786/dot_87786_DS1.pdf
• File Type:
  [PDF - 808.95 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:d5fff6e2c7e554e1fef84f65801d981c05da935862ae326944c5613424781e1d93ea1633bcc73822f67f26c7d4a351470baf7b5cacd658ec0cb510ea7080a392