Introducing a Novel Protocol for Collecting Annotated Images to Automate Concrete Structure Damage Severity Assessment
Bibliographic record
Abstract
Concrete structures like dams, bridges, and buildings require regular inspections to ensure public safety. Currently, these inspections are carried out manually, which demands significant human and financial resources and poses risks to inspectors due to accessibility challenges. A more promising approach involves automating inspections using drones and algorithms to detect and estimate cracks. This paper introduces a protocol for capturing high-quality images of concrete structures using a tripod and a high-resolution camera. The collected images were used to develop a robust algorithm to estimate crack widths and classify them based on severity according to the Manuel d'inspection des structures du Ministère des Transports et de la Mobilité Durable du Gouvernement du Québec. The algorithm's accuracy was validated by comparing its measurements with actual crack values obtained directly from the inspected structures. Additionally, the new database, now accessible to the public, contains numerous measurements that could prove invaluable for future research endeavors, inspiring breakthroughs in structural health monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".