Ambient modal identification, finite element model updating, and seismic analysis of bridges on Trans-Canada Highway
Bibliographic record
Abstract
This thesis features finite element model updating of two short-span concrete bridges, namely Gaglardi Way Underpass and Kensington Avenue Underpass. The main objective was to study the effect and determine the importance of finite element model updating by comparing the structural responses for the updated model to the preliminary model. The study was carried out by developing a finite element (FE) model and an operational modal analysis (OMA) model for each bridge. The FE model represented the analytical prototype of the actual structure, while the OMA model was used to extract the modal information for existing structure using the vibration data recorded under normal operating conditions from permanent sensors installed on corners and at mid-span of these bridges. The natural frequencies from OMA were set as a target for the FE model to match. The process of calibrating the analytical FE model to the match the modal information acquired from the experimental model is known as ‘Model Updating’. Having the frequency responses defined, a sensitivity analysis was conducted to determine the parameters that are most sensitive to change, based on which the FE model was automatically updated in an iterative manner. The modal assurance criterion (MAC) and mode shape responses were not used during calibration step since the vibration testing was not dense enough, however, they were solely used as a means of comparing the calibrated FE model to the experimental results. Once the objective of model updating was accomplished, a linear modal time history analysis was carried out using three ground motions having a low, medium range, and a very high peak ground acceleration (PGA), in addition to a fourth very low ambient level ground motion. Comparing the resulting absolute maximum base reactions and the mid-span structural displacements from updated model to the original model, it was concluded that the percentage changes were significantly high, therefore, the chance of original model being uncertain is very high for which model updating is an important and a highly effective technique, where possible, to generate a high confidence FE model that in best possible manner represents the behaviour of an actual structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".