Multi-Direction Bridge Model Updating Using Static and Dynamic Measurement
Bibliographic record
Abstract
This research present a multi-direction bridge finite element model updating method based on the static and dynamic test. A fiber optics structural health monitoring system was installed on the bridge site and 73 fiber optic sensors captured the static and dynamic data in local-level. A portable accelerometer system was used to record the ambient loading test and 15 force-balanced accelerometers were placed along bridge center to record the bridge global behavior. The original model was built according to the construction draw. The bridge model was updating by using multi-level test data. A new multi-direction model updating approach was established to separate the model updating into several stages based on the member’s direction. In each stage, the uni-direction members were updating in local-global level. This study found the multi-direction model updating can reduce the number of objective functions and variables in each stage and bridge model updating in the uni-direction has limited influence on the other directions. It is necessary to update steel girder bridge’s finite element model in the multi-direction in order to ensure the model’s accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".