Autonomous 3D vision‐based bolt loosening assessment using micro aerial vehicles
Bibliographic record
Abstract
Earlier identification of bolt loosening is crucial to maintain structural integrity and prevent system-level collapse. In this study, a novel drone-based 3D vision methodology has been proposed for autonomous bolt loosening assessment. First, a low-cost micro aerial vehicle with various types of sensors is designed. Second, a drone-based autonomous image collection method is proposed. Third, a 3D point cloud of the bolted connection is generated using the acquired images. Fourth, 3D point cloud processing methods are proposed to localize and quantify bolt loosening. The proposed method has been implemented on structural beam–column connections. The results show that the proposed drone-based data collection method can effectively acquire images for 3D reconstruction. The 3D point cloud processing methods can reliably localize and quantify bolt loosening at high accuracy. The proposed method provides a more robust and comprehensive evaluation of bolt loosening, compared to existing 2D vision methods, which process 2D images captured at a specific camera view.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".