Neural-network-based damage classification of bridge infrastructure using texture analysis
Why this work is in the frame
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Bibliographic record
Abstract
Damage in concrete structures can be assessed by analyzing the texture of surface deterioration using optical concrete imagery. This research proposes the application of an enhanced method of texture analysis, based on the signal processing technique of Haar’s wavelet transform in combination with the grey level co-occurrence matrix statistical approach, to characterize and quantify damage. Three different types of imagery, colour, greyscale, and thermography are evaluated for their effectiveness in representing surface deterioration. The multilayer perceptron artificial neural network classifier is applied on three different datasets: spatial, spectral, and a combination spatial–spectral dataset. Results show that the combination of textural and spectral data produced the highest overall accuracies; the thermography provided better classifications than the other types of imagery. Classifications based on the combination datasets were used to determine the different levels of damage in the concrete.
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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 | 0.001 | 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) | 0.000 | 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