Influence of brace modelling on the seismic stability response of tall buckling restrained braced frame building structures
Bibliographic record
Abstract
This paper examines the influence of the brace modelling on the seismic stability response of tall buckling restrained braced frames. In this approach the commonly used tri-linear model based on the backbone curves obtained from cyclic test data will be validated against calibrated models of Steel4 material in OpenSees. The validation involves evaluating the seismic response of 6-, 10-, 16-and 20-storey BRB frames using nonlinear pushover and response history analyses. These prototype structures are office buildings located in Vancouver, BC, Canada. This location was selected because the structures are exposed to three sources of earthquakes: crustal, deep in-slab subduction, and interface subduction earthquakes. Buildings in both models (tri-linear and Steel4) are analysed using OpenSees where the evaluation involves 33 scaled ground motions representative of the sources of earthquakes. For the Steel4 model, an enhanced optimisation algorithm is used to calibrate the 20 parameters and obtain high-level correlation with past cyclic tests data. Displacements, storey drifts, and force demands are evaluated for all prototype buildings for both BRB models. It is found that the tri-linear model mitigated the negative stiffness due to P-Delta effects, resulting in storey-drifts within the design limit (2.5). On the contrary, buildings with the Steel4 BRB model experienced excessive drifts, especially under the interface subduction earthquakes. © The 17th World Conference on Earthquake Engineering.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".