Monitoring of the Great Belt Bridge hanger vibrations and expansion joint movements using Digital Image Correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Civil infrastructure system owners are often faced with an increasingly impossible set of management challenges. Informed decisions on timely intervention for effective bridge maintenance activities rely on good quality, accurate and reliable asset condition data. Digital image correlation (DIC) is a noncontact photogrammetry technique that can be used for monitoring by imaging a bridge component periodically and computing strain and deformation from images without traffic disruption. This paper describes the use of DIC for the monitoring of the Great Belt Bridge wind‐induced hanger vibrations and temperature‐induced movements of the expansion joint. Both DIC measurements provided previously unavailable data and informed next steps with respect to the maintenance strategy. To the authors knowledge these are one of the first such vision‐based structural health monitoring campaigns carried out on a suspension bridge.
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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 | 0.000 |
| 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) | 0.000 | 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 it