A Vision-Based System for Structural Displacement Measurement
Why this work is in the frame
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Bibliographic record
Abstract
Current structural displacement measurement methods for structural health monitoring (SHM) are based on displacement data of acceleration, strain, laser doppler vibrometer, Light Detection and Ranging (LiDAR), total station, and Global Navigation Satellite System (GNSS) measurements. However, these methods are time consuming, labor intensive, limited in spatial and temporal resolution, costly and restricted to certain applications. For these reasons, a new method to measure structural displacements is needed. This study examines a novel structural displacement measurement method using a vision-based system coupled with computer vision algorithms. To test and evaluate the performance of the proposed method, seven tests were performed with varying focal lengths and 89 distance measurements using a calibrated meter stick. Results show that the error in a distance measurement decreases to within 0.02% as the measured distance increases for a fixed focal length. Furthermore, the error in a distance measurement decreases to within 1.15% as the focal length increases. Therefore, the proposed methodology is recommended for efficiently measuring structural displacements ranging from 1 mm to 1000 mm with errors less than 1.15%.
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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