Shake table testing of bookcase – partition wall systems
Bibliographic record
Abstract
This paper describes the seismic (shake table) tests conducted on bookcase – partition wall systems. These nonstructural building components can be considered acceleration sensitive (or motion sensitive) rather than drift sensitive. The shake table floor motions used for the seismic testing are described in a companion paper. One bookcase fully loaded with books and two different cantilevered partition wall systems were considered in the shake table tests. Nine different configurations of these free-standing nonstructural building components were tested. For each configuration, three different seismic hazard levels were considered for the motions at the second floor level of a six-storey building designed for two different densely populated Canadian cities (Montréal and Vancouver). A total of 485 shake table tests were conducted in this experimental investigation. The experimental results indicated that pounding between unanchored bookcases and partition walls is very beneficial to the dynamic response of the bookcases, as it prevents resonance from occurring. Also, the seismic performance of bookcases improved dramatically by the proper installation of seismic restraint systems. Experimental fragility curves for overturning of tall bookcases are presented.Key words: bookcases, earthquakes, fragility, interior partition walls, nonstructural, operational and functional components, pounding, seismic restraints, shake table.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".