First Results of the Application of Infrared Thermography to the Crack Inspection in Wooden Beams
Bibliographic record
Abstract
Wooden structures are exposed to degradation during their lifetime.There are a large number of wooden structures around the world, some of them historical which are patrimony of humanity.Structural health inspection is a priority task to be applied to such structures in order to prevent or mitigate the effect of degradation, but nowadays this health inspection is generally manual.Therefore, the development of methodologies that allow to automate the structural health inspection is a goal of maximum priority.Among the most common defects that appear in wooden structures due to degradation are the cracks.Although the structural health inspection of wooden structures is generally manual, some methods have been applied for the automatic inspection of cracks using different techniques, such as laser scanner or photogrammetry.However, the estimation of crack depth is beyond the scope of these techniques.Moreover, InfraRed Thermography (IRT) has proved to be a useful tool for estimating the depth of different types of defects.Then, this work introduces IRT as a technique for the automatic inspection of superficial cracks in wooden beams.Specifically, the width, long and depth of the cracks are estimated through the development of two different methodologies: (i) the first method consists of the analysis of the thermal images acquired without previous thermal excitation on the wooden beams, and (ii) the second method consists of the analysis of the thermal image sequence acquired during the cooling after a thermal excitation in each wooden beam.The results of this work show that both methodologies can be used as a basis for the future automatization of crack inspection in wooden beams.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".