Passive infrared thermography for subsurface delamination detection in concrete infrastructure: Inference on minimum requirements
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a computational approach for inferring the minimum requirements for the nondestructive inspection of subsurface delamination in outdoor concrete structures using passive infrared thermography (IRT). The non-linear numerical system was solved using the Finite Element Method (FEM). Complete verification and validation of the numerical model were performed through the analysis of experimental and computational errors, as well as through the comparison of computational outputs of thermal gradients with the contrast values measured in an experiment with solar radiation and passive IRT. The results of accuracy and precision of the computational simulation approach were found to be adequate, from a practical perspective, for the intended use of the model, with the thermal gradient values having an uncertainty of 0.080 ± 0.91 °C and -0.016 ± 0.91 °C for the concrete slab and column sample, respectively. Furthermore, the developed model was used to perform a one-year analysis of the studied case, in order to determine the approximate radiative heat flux required to identify defects with different size-to-depth (S/D) ratios in various concrete components with distinct solar exposures. Finally, the relationship between the calculated radiative heat flux and thermal contrast with the respective environmental variables in place was analyzed graphically. • Multiphysics modeling of passive infrared thermography (IRT) for concrete inspection. • Comprehensive verification and validation of the computational model. • Inference on the minimum radiative heat flux required for detecting delamination. • Required heat flux varied with defect size-to-depth (S/D) ratio and solar orientation. • Limited detection for defects with unfavorable solar orientation and small S/D ratio.
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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