Field monitoring of a bridge using digital image correlation
Bibliographic record
Abstract
In order to provide quantitative data to supplement visual inspections and numerical modelling as part of the overall bridge assessment process, inexpensive sensor technologies that provide useful and accurate data are required. Digital image correlation (DIC) is a measurement technique that can be used to provide displacement, crack width and strain data from the same set of digital images. This paper outlines a field application using DIC to measure static displacements at varying load levels and dynamic displacements at varying vehicle speeds of a reinforced concrete bridge. The DIC displacement measurements provided by two different camera systems are shown to be in good agreement with the results from a conventional displacement transducer for both the static and the dynamic tests. The results of the static tests with varying load levels are then used to illustrate how the bridge response can be evaluated by examining the load deflection behaviour, which remained in the linear elastic range throughout the loading. The dynamic displacement data were then compared to the static response to determine what, if any, difference in response there was to dynamic loading and thus if the use of impact factors should be considered when assessing this bridge.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".