Determination of Optimum Configurations for Steel-Braced Frames with Segmental Elastic Spines
Bibliographic record
Abstract
A simplified analysis method is proposed to predict the response of segmental elastic spine–braced frames (SESBFs) and select the appropriate truss-segment configuration for a given frame. The method relies on a simplified structure model that can reproduce both the elastic flexural response and inelastic shear response of the braced-frame system. The proposed simplified model is described, and a flowchart is presented to illustrate the steps leading to the frame properties required to achieve the optimum seismic drift response for a given truss-segment configuration. In the design, the process is repeated for different potential truss-segment configurations, and their seismic responses are compared to select a suitable configuration for the structure. The application of the proposed procedure is illustrated for a 24-story building structure located in Vancouver, British Columbia, Canada. Five different truss-segment arrangements were investigated, and two configurations were identified as appropriate for the structure. Final design of the four most promising candidates was performed to confirm the findings from the preliminary design, and the comparison confirmed that the proposed method and simplified analysis model are suitable tools for the preliminary design of SESBFs.
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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) | 0.001 | 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".