Experimental investigation of subsurface defect detection in concretes by infrared thermography and convection heat exchange
Bibliographic record
Abstract
Abstract Infrared thermography (IRT) is a non-destructive technique capable of detection and localisation of hidden subsurface defects in components of transportation infrastructure, such as concrete bridges, thereby contributing to structural health monitoring (SHM). Addressing the lack of research on subsurface defect detection in concretes by convection heat exchange, and regarding the importance of laboratory studies for proper implementation of IRT, this paper presents results from recent laboratory investigations of IRT on concrete slabs with simulated hidden defects using a convective thermal excitation mechanism. The concrete slabs in this study had simulated defects ranging 5–25 mm in depth from the surface. These studies show the effect of initial temperature, heating/cooling process, temperature range and defect depth on thermal contrast in the concrete slabs. Furthermore, this paper compares the performance of the IRT as a non-contact sensor and thermocouples attached to the surface, in the evaluation of the thermal contrast on slabs with various defect depth. The dependence of maximum thermal contrast on the initial temperature and defect depth is explored using multivariate linear regression.
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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 | 0.001 |
| 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 | 0.001 |
| 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".