Automated Scanning of Concrete Structures for Crack Detection and Assessment Using a Drone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned Aerial Vehicles are becoming more accessible, opening the way to monitoring and inspecting civil infrastructure, even in challenging and dynamic environments. Indeed, using UAV s contributes to safer, faster and more accurate inspections, often beyond what the human being can detect. This paper proposes a path-planning method for an autonomous scan mission using a UAV with an onboard camera for concrete structure inspection. The main objectives are ensuring maximum coverage of the structure and collecting and transmitting images to estimate the extent of damage caused by cracks. The proposed solution is integrated and evaluated using the Software-In-the-Loop Simulation. The results show that the proposed algorithm allows robust scanning with the least energy dissipated by batteries during the mission.
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.
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