Seismic performance of low- and medium-rise chevron braced steel frames
Bibliographic record
Abstract
This paper describes the seismic behaviour of chevron steel braced frames for 2-, 4-, 8-, and 12-storey steel building structures. Two different design approaches were considered: one that corresponds to current CSA-S16.1 seismic provisions for braced frames with nominal ductility with an R factor of 2.0, and one in which the beams are sized to develop a fraction of the yield tension capacity of the bracing members. In this second approach, an R factor of 3.0 was used for determining the seismic loads and chevron bracing with stronger beams capable of developing 100%, 80%, and 60% of the brace yield load were examined. The results show that current S16.1 provisions for chevron braced frames may lead to systems that are prone to dynamic instability for 4-storey and taller structures. Chevron bracing with stronger beams exhibits a more stable inelastic response and can be used for structures up to 8 storeys in height. For 2- and 4-storey buildings, chevron braced frames with beams designed to develop only 60% of the brace yield resistance can be used. The analyses also show that the force demand in brace connections, beams, and columns as determined from capacity design provisions agree well with that anticipated under strong ground motions.Key words: earthquakes, seismic design, steel, structures, braced frames, bracing members, beams, columns, connections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".