Passive infrared thermography for subsurface delamination detection in concrete infrastructure: Capabilities
Bibliographic record
Abstract
Passive infrared thermography (IRT) has been introduced as a faster, safer, and contactless alternative for the nondestructive inspection of subsurface delamination in concrete infrastructure. However, some uncertainties remain, such as the absence of an inspection protocol to inspect multiple concrete components and the dependence of the technique’s performance on environmental conditions and solar energy. This study presents a proof of concept about the capabilities of passive IRT in detecting subsurface damages in multiple concrete components under variate solar exposure. The passive IRT capabilities are explored by analyzing the thermal sequences obtained from samples of artificially damaged concrete structures inspected over 24 h in various environmental conditions over three seasons. As a result, damages with a size-to-depth (S/D) ratio between 1.0 and 2.7 were detected using the thermal contrast method. Furthermore, the implementation of signal processing techniques yielded an improvement in capabilities ranging from 15% to 52%. Finally, a procedure for acquiring data while inspecting multiple concrete components using passive IRT is proposed. • Infrared thermography detection of concrete delamination under varying solar exposure. • Procedure to optimize data collection in concrete under varying solar exposure. • Detection of damages up to 8.0 cm depth in concrete with a 1.0 width-to-depth ratio. • The specific capability varied based on the component type and solar orientation. • Signal processing mostly improved contrast-to-noise ratio and detection capabilities.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".