Quantification of subsurface defects in reinforced concrete of bridges by unsupervised segmentation of IR images
Bibliographic record
Abstract
This paper presents segmentation analysis of Infrared (IR images of reinforced concrete (RC blocks for characterisation and quantification of corrosion defects using unsupervised clustering. The IR images used in this study were collected during cool down process of RC slabs to laboratory environment temperature through convection heat exchange. The RC slabs were cast from a normal strength mix, typical for bridge construction in the UK and Ireland. The slabs had two steel rebars with protruding ends that were used for accelerated corrosion setups. Unsupervised clustering was conducted on IR images by applying k-means clustering method on normalised temperature readings in a region of interest. In this paper, the performance of clustering method to distinguish between environmental or surface effects and true bridge anomalies is studied, and the corrosion-affected concrete is quantified. Variation of thermal contrast and quantity of defective concrete during the experiments as well as discussion of the results in context provides a basis for improved implementation of IRT for RC structures and contributes to wider objectives of structural health monitoring (SHM).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".