Passive infrared thermography for subsurface delamination detection in concrete infrastructure: Inference on minimum requirements
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".