Digital Image Correlation (DIC) for Strain and Displacement Mapping on Concrete Containment Structures during a Leak Rate Test
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Bibliographic record
Abstract
Digital Image Correlation (DIC) 3D stereo-optical systems were used to record displacementand strain-fields during a leak rate test at VeRCoRs (Vrification Raliste du Confinement des Racteurs), a 1/3-scale mock-up of a 1300 MW nuclear reactor building.Two identical 12 Mpx (Megapixel) DIC systems were installed on the floor underneath the dome of VeRCoRs to record an area of 3370 mm x 1700 mm on the outer wall of the containment mock-up during the leak-rate test from March 14 th to March 17 th , 2023.The area of interest on the external concrete wall of VeRCoRs was painted white and patterned with 5 mm black dots, as required for the field-of-view and working distance of the DIC cameras.The DIC systems ran continuously acquiring images at 1 frame per min.Every 10 th frame was processed corresponding to 10 minutes of changing pressure inside the mock-up.GOM Suite 2022 software package was used for image processing.The deployment of two DIC 3D stereo-optical systems during the leak-rate test at VeRCoRs was successful.The external area of the concrete wall during the pressure tests, recorded by the two DIC systems, did not show cracks or unexpected features.Slightly higher strain was observed in the field-of-view of the primary DIC system compared to the strains recorded by the secondary DIC system at maximum pressure during testing.Several hours after completing the pressure test, remaining strain was observed within the recorded area.The measured strainfields were within the measured values obtained with the embedded strain sensors.
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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 | 0.001 |
| 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