Vision-Based Correlated Change Analysis for Supporting Finite Element Model Updating on Curved Continuous Rigid Frame Bridges

Inspection on curved continuous rigid frame bridges (CRFBs) is necessary but challenging due to the complexity of bridge structures. Revealing the “true” condition of CRFBs requires accurate field observations and reliable interpretation method to interpret the observed spatial changes. Non-contact vision-based systems (e.g., LiDAR) offer a promising alternative for structure change detection. Unfortunately, appropriate interpretations of the detected changes of CFRBs is challenging due to coupling deformations of connected bridge elements. Unreliable interpretations of change correlations of bridges can profoundly affect the effectiveness of maintenance planning. Finite element models (FEMs) developed based on the as-designed conditions could help explain how structural changes influence each other. Updating parameters of FEMs could simulate how deteriorations of structural elements influence the patterns of correlated structural changes. Unfortunately, FEM updating can be a multiple-solution problem—multiple possible structural properties and boundary conditions could produce similar geometric shapes of the structure under the same loading condition. In many cases, the combinations of FEM parameter values that form the solution search space can be exponentially large. Examining the exponentially large number of combinations of FEM parameter updates is computationally intractable for large structural systems. As a result, existing studies that use field data for updating FEM and interpreting structural changes could only handle structures with a few elements in controlled environments. This study established a method for FEM updating based on vision-based correlated change analysis. The method focuses on integrated used of change correlation analysis and FEM simulations for reducing the computational complexity. The results indicate that the correlated change analysis provides the potential of eliminating possibilities of certain combinations when updating the FEM for improved computational efficiency.

• Record URL:
  https://doi.org/10.1061/9780784482858.042
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482858
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Sun, Zhe
  • Shi, Ying
  • Xiong, Wen
  • Tang, Pingbo
• Conference:
  • Construction Research Congress 2020
  • Location: Tempe Arizona, United States
  • Date: 2020-3-8 to 2020-3-10
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 380-389
• Monograph Title: Construction Research Congress 2020: Infrastructure Systems and Sustainability

Subject/Index Terms

• TRT Terms: Computer models; Condition surveys; Continuous girder bridges; Curved bridges; Finite element method; Machine vision; Rigid structures; Structural deterioration and defects; Structural health monitoring
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01760296
• Record Type: Publication
• ISBN: 9780784482858
• Files: TRIS, ASCE
• Created Date: Nov 9 2020 3:01PM