A Vision-Based System for Structural Displacement Measurement
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%.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".