Leveraging Public Safety and Enhancing Crack Detection in Concrete Bridges using Deep Convolutional Neural Networks
Bibliographic record
Abstract
The safety of transportation networks is intrinsically linked to the reliability of their infrastructure. Throughout their life cycle, bridges are subject to numerous types of damage that are generally monitored using several Structural Health Monitoring methods. Cracks, the most common defect encountered in concrete bridges, have been the main focus of numerous research works investigating several frameworks based on computer vision for crack detection automation. This paper proposes a pixel-level crack detection method in concrete bridge surfaces leveraging the popular semantic segmentation model UNet and two benchmark public crack datasets. The results have demonstrated the efficiency of the proposed framework as the model achieved a mean Intersection over Union of 76.92% and an F1-Score of 70.45%. The trained model was further tested on two high-resolution images to detect cracks in complex and noisy concrete bridge surfaces, and the width of the cracks was estimated using image processing techniques. The results showcase the potential use of the segmentation outputs to analyze cracks and assess their severity level and impact on bridges' structural safety.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".