Highly Efficient Model Updating for Structural Condition Assessment of Large-Scale Bridges

Details

• Creators:
  Yan, Guirong ; Zhou, Linren
• Corporate Creators:
  University of Texas at El Paso
• Corporate Contributors:
  United States. Federal Highway Administration ; United States. Dept. of Transportation. Research and Innovative Technology Administration ; Texas. Dept. of Transportation ; Rutgers University. Center for Advanced Infrastructure and Transportation
• Subject/TRT Terms:
  • Cable stayed bridges
  • Bridge design
  • Condition surveys
  • Structural health monitoring
  • Quadratic equations
  • Algorithms
  • Model Updating
  • Condition Assessment
  • Bridges
  • Research Hub
• Publication/ Report Number:
  CAIT-UTC-016
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Rutgers University. Center for Advanced Infrastructure and Transportation
• NTL Classification:
  NTL-HIGHWAY/ROAD TRANSPORTATION-Bridges and Structures
• Abstract:
  For efficiently updating models of large-scale structures, the response surface (RS) method based on radial basis functions (RBFs) is proposed to model the input-output relationship of structures. The key issues for applying the proposed method are discussed, such as selecting the optimal shape parameters of RBFs, generating samples by using design of experiments (DOE) and evaluating the RS model. The RS methods based on RBFs of Gaussian (GA), inverse quadratic (IQ), multiquadric (MQ) and inverse multiquadric (IMQ) are investigated. Results have demonstrated that RS methods based on RBFs can achieve a high approximation accuracy and better performance than the RS method based on polynomial function. The proposal method has been validated numerically and experimentally on a cable-stayed bridge model.
• Format:
  PDF
• Funding:
  DTRT12-G-UTC16
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29019/dot_29019_DS1.pdf
• File Type:
  [PDF - 1.88 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:c3d364c63deed846f4f824243a7227e55258a0bfbd6f061e5d14edb4813c0ccb