Active infrared thermography applied to defect detection and characterization on asphalt pavement samples: comparison between experiments and numerical simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This work is devoted to the application of active infrared thermography to defect detection in pavement structures. The challenge is to localize and to determine some properties of defects (e.g. shape and depth) into a highly heterogeneous material. Experimental work was carried out in laboratory conditions using a pavement sample containing two defects (wood and air). Pulsed thermography results were compared with FLUENT numerical simulations. Different preliminary approaches were investigated to analyze data: singular value decomposition of infrared image sequences, contrast image methods and computation of thermal effusivity considering a heat transfer model in a semi-infinite material. This last method is more sensitive to experimental conditions such as the presence of natural convection at a sample surface. However, all methods allow detection of one defect into the pavement sample. Keywords: active infrared thermographynon-destructive testingbitumen concreteheat transfer
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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 | 0.000 |
| 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