Operational modal analysis, model updating, and seismic analysis of a cable-stayed bridge
Bibliographic record
Abstract
The Port Mann Bridge is currently one of the longest cable-stayed bridges in North America and the second widest bridge in the world. It is a cable-stayed bridge consisting of 288 cables, two approach spans made of concrete box girders and precast deck panels, and a main span consisting of steel girders and cross beams with precast deck panels. This work sets out to accomplish three main goals: study the dynamic behaviour of the Port Mann Bridge, calibrate the finite element model, and study the effects of model updating using a seismic analysis. The dynamic behaviour of the Port Mann Bridge’s main span is studied using experimental data from field ambient vibration tests and from a structural health monitoring network. A finite element model is created by importing a version of the structural designer’s model and editing it based on design drawings. In order to assess what parameters would be feasible to calibrate, a sensitivity analysis is carried out using various material properties and boundary conditions. The model is then updated to match the experimental analysis results by varying multiple parameters. Finally, the calibrated model is compared to the original model by completing a linear time history analysis. A suite of ground motions were selected and scaled to match specific points on the response spectrum corresponding to the first few periods of the structure. Multiple critical locations are monitored in the time history analysis, and data from these locations are compared before and after calibration to examine the effect of model updating. The study concludes that model updating has a large effect on the predicted seismic behaviour of the bridge, which proves the importance of calibrating finite element models and maintaining physically meaningful parameters. It also shows that having a structural health monitoring program is very important for current and future research endeavours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".