Tracking of Defects in Reinforced Concrete Bridges Using Digital Images
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel approach for the periodic detection of defects in concrete bridges based on a set of dimensionless metrics pertinent to fractal analysis of digital images. Visual inspection and image subtraction methods are generally used for the periodic comparison of structural conditions. However, such approaches, such as visual inspections, have been identified with several limitations, but they are time consuming processes and decisions are influenced by individual experiences. Likewise, image subtraction method requires image registration, which is a difficult process in achieving precise image registration for reliable outputs. This research uses fractal analysis of digital images to track surface defects by estimating their fractal dimensions. The results of the fractal analysis of concrete beams are compared with the results of spectral analysis which requires images to be translated from spatial domain to frequency domain. The proposed method successfully generates unique metrics necessary for change quantification which overcomes the limitation of the existing approaches.
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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 | 0.000 | 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 it