Seismic Design, Analysis and Testing of a Friction Steel Braced Frame System for Multi-Storey Buildings in Vancouver
Bibliographic record
Abstract
This article describes the application of the Friction Braced Frames (FBF) system for 4-and 10-storey buildings located in Vancouver, BC, in Canada. The FBF is coupled with a secondary moment resisting frame that provides back-up elastic stiffness and re-centring capacity that contribute to reduce storey drifts and more evenly distribute seismic energy dissipation over the frame height. In this study, the energy dissipation components consist of Pall friction devices and the system was designed using a conventional force-based method. The moment frame was proportioned to remain essentially elastic under the design seismic displacements. The seismic response of the system is examined through nonlinear response history dynamic analysis. An exhaustive test program was developed to verify the capacity of the system to sustain the anticipated seismic demand. Full-scale testing was performed on brace sub-assemblages and individual brace specimens equipped with friction elements. The experimental program included full-scale dynamic testing under real-time seismic displacement histories as obtained from response history analysis. The numerical simulations and test program showed that the dual FBF system represents an effective system for enhanced seismic response of multi-storey building applications in high seismic regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".