Revolutionising Visual Bridge Inspection: A Deep Learning Approach for Automated Concrete Bridge Distress Identification & Analysis of Results
Bibliographic record
Abstract
Concrete bridges are vital infrastructure assets, yet their inspection often relies on labour-intensive, time-consuming, and sometimes subjective visual assessments.This study addresses these challenges by harnessing the power of Artificial Intelligence (AI) and Deep Learning (DL) for streamlined bridge inspection.Building upon the limitations of traditional methods, an enhanced YOLOv8s model is developed and trained on a refined CONBRID-YOLOv8 dataset.This dataset is specifically designed to minimize false positives, a common issue in concrete bridge defect detection.The integration of real-time data visualisation tools further empowers inspectors to optimize maintenance planning, ultimately enhancing bridge safety and longevity.The model exhibits exceptional performance in detecting and classifying prevalent concrete defects such as cracks, spalling, exposed bars, corrosion stains, and efflorescence.Through rigorous experimentation and analysis, the new model achieved a strong F-1 score of 0.75 and a mAP of 0.738 after 300 epochs.Real-world field testing underscores the model's practical effectiveness.Pioneering data visualisation techniques provide inspectors with the tools to rapidly interpret complex results and confidently prioritise maintenance strategies.This AI-powered approach represents a significant advancement in bridge inspection practices.By addressing the limitations of traditional methods & existing DL models, this study offers a more efficient, accurate, and objective solution for ensuring the safety and longevity of critical infrastructure assets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".