Curvature Monitoring of Beams Using Digital Image Correlation
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Bibliographic record
Abstract
A method for measuring longitudinal strains with the height at a section, and thus the curvature, using a technique based on digital image correlation (DIC), is presented. The background to this technique is introduced as well as previous work in this area. The accuracy of DIC under ideal conditions is established using artificially generated images that represent beams with various curvatures. The practical accuracy of DIC is established by comparing the strains measured using DIC to those predicted by elastic theory and measured using strain gauges for a steel beam. The correlation between these results is found to be excellent. DIC is then used to measure curvatures in RC beams and these results are compared with analytically predicted results with good agreement. The choice of an appropriate gauge length for RC is discussed and is shown to be one of the significant advantages of using DIC as opposed to strain gauges in both laboratory testing and field monitoring of bridge structures.
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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 | 0.002 |
| 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