Integration of active thermography and ground penetrating radar for the detection and evaluation of delamination in concrete slabs
Bibliographic record
Abstract
Subsurface delamination is one of the main damage mechanisms that affect the integrity of structural components. Its detection effectively and reliably is a crucial step for in-service assurance and avoiding further accidents. In that scenario, it is necessary to explore alternatives to current inspection practices so that effective non-destructive methods can be implemented in the field. This paper investigates the application of infrared thermography and ground penetrating radar to detect and evaluate subsurface delamination in concrete components. To this aim, laboratory specimens made of reinforced concrete with Teflon inserts to simulate internal delamination are inspected with the step-heating approach. The IR data collected during the heating and cooling process is then evaluated and processed with pulsed-phase thermography and principal component regression. The extension or severity of the delamination is then evaluated with ground penetrating radar. The results will be evaluated to determine the applicability of the methods at larger scales.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".