Subsurface defect detection in concretes by active infrared thermography
Bibliographic record
Abstract
This paper presents observations from an active infrared thermography (IRT) experiment about structural monitoring by taking advantage from solar irradiance as a clean and renewable source of energy for thermal excitation. This contributes to reduction of carbon emissions associated with maintenance of existing concrete infrastructure and ensuring their extended life, and safe operation. The models in these observations were five concrete slabs made from a typical mix used for bridge construction in the UK, with simulated subsurface void (representing the defect) at depths of 5 to 25 mm (5mm increment) at the centre of slabs, and one slab without simulated defect. This study was conducted during a sunny afternoon. A sequence of IR images was collected for each slab (six sequences in total), and these sequences were used to calculate the average thermal contrast on surface of the slabs and evaluate its variation with depth of subsurface defect. Finally, the trend of thermal contrast is compared with the trend of thermal contrast from excitation by IR heater to highlight the limitations and future research needs for subsurface damage detection using solar irradiance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".