Evaluation of Vision-Based Measurements for Shake-Table Testing of Nonstructural Components
Bibliographic record
Abstract
During an earthquake, freestanding equipment and contents in a building may experience large complex 3D motion. Depending on the geometry, mass distribution, and support mechanism (e.g., wheels, casters, legs) of the object, this motion may include rolling, sliding, twisting, and rocking—potentially resulting in overturning or impact with building occupants, neighboring walls or other objects. Measuring this complex motion by traditional, contact-type displacement sensors is challenging. Owing to recent advances in video capture sensors and image processing techniques, vision-based motion tracking and measurement have been introduced as a practical, economical, and fairly accurate measuring method. This paper presents a procedure utilized to evaluate the accuracy of a consumer-grade camera for the purpose of measuring the motion of a piece of medical equipment during shake-table testing. The fixed-focal length camera considered in this study can capture video recordings with different resolution and frame rates. During experimental testing, the camera is positioned at a distance from the target (as it would be in a real application) to track the motion of four LED lights attached to the shake table. The capabilities of the camera are evaluated using as input a signal with varying frequency and amplitude. A wavelet approach is proposed and utilized in order to synchronize the vision-based displacement measurement with the output of the displacement transducer installed on the shake table, to be later used in accuracy assessments. Absolute and relative error curves are presented to evaluate the errors in the frequency range of interest for the actual experiments. Finally, contour plots are proposed that specify the displacement, velocity, and acceleration accuracy of vision-based measurements, which can be used in future applications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".