Efficient and Cost-Effective Rating of Critical Members of Steel Truss Railroad Bridges Supported by Field Test Data

Details

• Creators:
  Anand, Rahul ; Tripathi, Sachin ; Malla, Ramesh B.
• Corporate Creators:
  University of Connecticut. Department of Civil and Environmental Engineering ; University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Corporate Contributors:
  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:
  Critical Members Model Updating
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Connecticut
• Edition:
  Final Report
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  Assessing the load-carrying capacity of aging steel truss railroad bridges is challenging, due to significant uncertainties in material properties, deterioration, and support conditions. This study presents a comprehensive methodology for efficient, cost-effective load rating of critical bridge members through high-fidelity computational modeling, using the century-old Cos Cob Bridge in Connecticut as a case study. The approach integrates non-contact field measurements using Laser Doppler Vibrometer (LDV) with advanced finite element (FE) model updating. An initial FE model, developed from original design drawings, was calibrated using dynamic data (deflection time-histories and natural frequencies) collected under operational train loads. A sensitivity analysis first identified critical uncertain parameters, such as steel stiffness, joint flexibility, and support stiffness. These influential parameters were then systematically optimized using a real-coded genetic algorithm (RCGA) to minimize discrepancies between simulated and observed responses. The resulting calibrated model accurately replicated the bridge’s dynamic characteristics—including natural frequencies, mode shapes, and mid-span deflections—matching the LDV field data within a few percent. A refined load rating, performed on the validated model in accordance with AREMA standards, confirmed that the bridge meets current operational safety requirements. This research demonstrates that combining advanced non-contact diagnostics with sensitivity-guided optimization provides a robust framework for accurate load capacity assessment, ultimately supporting more reliable infrastructure management decisions.
• Format:
  PDF
• Funding:
  69A3551847101
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:0a12f8206e221bc82b90db5f482cde992bb7ae8f1d53df04f5ffed25189b82c833efa6a7056b627e9b60aeb6c3d13e5030fce9924e24af3ac71ad42cfd12a06c
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88734/dot_88734_DS1.pdf
• File Type:
  [PDF - 2.46 MB ]