Shake table investigation on the seismic performance of hospital equipment supported on wheels/casters
Bibliographic record
Abstract
Summary This paper presents an experimental investigation on the seismic response of medical equipment supported on wheels and/or casters. Two pieces of equipment were tested: a large ultrasound machine and a cart carrying smaller medical equipment. In the first phase, the resistance of the wheels and casters of the equipment was characterized through a controlled‐displacement procedure on the shake table. In the second phase, an extensive shake table test program was carried out to investigate the seismic response of the equipment. The input signals for the shake table tests included floor motions of a four‐story steel braced‐frame hospital designed to satisfy seismic requirements of a site in the Los Angeles area. The results of 96 shake table tests reported in this study include the seismic performance of the equipment under both unlocked and locked conditions, located on various floor levels of the building. It was observed that engaging the casters' locking mechanism does not necessarily decrease the relative displacement. The displacement response was sensitive to the excitation intensity and the orientation of the equipment with respect to the input excitation. Based on the experimental observations, appropriate structural engineering demand parameters associated with the relative displacement and relative velocity demands of the equipment are proposed and used to develop conditional probability curves. Finally, in an effort to extend the results of this experimental study to similar equipment on wheels/casters, the performance of a simple numerical model in predicting the peak seismic demands is evaluated. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".