Analytical Study on the Predictability of Controlled Rocking Frame and Podium Systems
Bibliographic record
Abstract
ABSTRACT While rocking systems are increasingly researched and developed as a promising approach to achieving seismic resilience, several studies have identified the unpredictability of their dynamic response as an obstacle to their wider adoption. Despite a significant body of literature which has identified and studied the chaotic behaviour of unenhanced rocking frames, other studies have shown that practical applications of controlled rocking systems demonstrate a more predictable response that is similar to that of more conventional yielding systems. This paper examines the differences between these two bodies of work and identifies parameters shown to improve rocking predictability. Subsequently, a perturbation study conducted on a controlled rocking podium system subjected to historic ground motions is employed to study the influence of each parameter on the predictability of such systems. The results reveal that adding supplemental damping, increasing the contribution of the superstructure properties on the rocking system behaviour and limiting the rocking rotation effectively reduce the sensitivity of rocking systems to perturbations in the rocking parameters. In addition, adding auxiliary stiffness was found to be a viable method to improve the stability of these systems but did not significantly enhance predictability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".