Textural Analysis For Crack-Detection Using Infrared Thermography, Visual Color, And Greyscale Concrete Imagery
Bibliographic record
Abstract
Imaging-based inspection methods are increasingly being employed for damage assessment in concrete structures due to the development of advanced non-destructive testing (NDT) techniques. These methods can provide quantitative information while reducing the time and cost involved, compared to inspections based solely on conventional visual approaches. However, in order to extract accurate data from the images, efficient image analysis methods need to be developed. This study proposes the application of the grey level co-occurrence matrix (GLCM) texture analysis approach, through which surface deterioration features in the concrete imagery are extracted. An artificial neural network (ANN) classifier is also employed to obtain damage information, such as the total amount of superficial cracking, as well as the total length, and range of crack widths. These methods were applied to thermographic, visual color and greyscale images of concrete blocks that were exposed outdoors for ten years, as well as slabs that were kept indoors, all specimens exhibiting various levels of alkali-aggregate reaction (AAR) damage. The results show that all three types of imagery are relatively effective in characterizing and quantifying crack damage; however, the infrared thermography produced more accurate results compared to the visual color, and grey scale images.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".