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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.
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.
Vision-Based Correlated Change Analysis for Supporting Finite Element Model Updating on Curved Continuous Rigid Frame Bridges
Sun, Zhe (author) / Shi, Ying (author) / Xiong, Wen (author) / Tang, Pingbo (author)
Construction Research Congress 2020 ; 2020 ; Tempe, Arizona
Construction Research Congress 2020 ; 380-389
2020-11-09
Conference paper
Electronic Resource
English
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