Digital Image Correlation (DIC) for Strain and Displacement Mapping on Concrete Containment Structures during a Leak Rate Test
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 (Vérification Réaliste du Confinement des Réacteurs), 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".