Seismic Design of Steel Chevron and Split‐X Concentrically Braced Frames
Bibliographic record
Abstract
ABSTRACT This paper aims to (1) investigate how various design parameters affect seismic response of steel chevron and split‐X braced frames, (2) advance understanding of their seismic behaviour and (3) propose new system‐specific guidelines for accurately estimating their seismic demands and for achieving enhanced seismic performance. Initially, 12 frames are selected and designed in accordance with Canadian design provisions. Frames are numerically modelled with a fibre‐based technique, and nonlinear response history analysis is performed to evaluate the effect of key design parameters on their seismic behaviour, including drift response, beam deflection, brace axial force, column moment demand and beam yielding. Subsequently, 380 additional frames are generated by adjusting the brace cross‐sections of the initial set. These frames are then dynamically analysed to develop mathematical expressions capable of predicting column flexural demands and identifying the location of drift concentration. Furthermore, design recommendations are proposed based on the results of dynamic analyses for accurately estimating beam demands in split‐X braced frames and in chevron braced frames with elastic and yielding beams.
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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) | 0.001 | 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".