Evaluation of AISC Seismic Design Methods for Steel Multi-Tiered Special Concentrically Braced Frames
Bibliographic record
Abstract
Steel multi-tiered concentrically braced frames (MT-CBFs) are commonly used in North America as a lateral load resisting system of tall single-story buildings. Past studies show that MT-CBF columns designed in accordance with the 2010 AISC Seismic Provisions are prone to buckling due to a high axial compression force combined with in-plane bending moments caused by the nonuniform distribution of inelastic brace deformations along the frame height. Special design provisions have been introduced in the 2016 AISC Seismic Provisions to address flexural demands imposed on MT-CBF columns and prevent column instability. In this paper, the seismic design methods for multi-tiered special concentrically braced frames are evaluated using the nonlinear finite element analysis method. A two-tiered special concentrically braced frame was then created, and nonlinear static and dynamic analyses were performed to evaluate the seismic performance of both frames. Analysis results confirmed that the inelastic deformations in the frame designed using the 2010 requirements are not uniformly distributed but rather concentrated in one of the tiers and cause column instability under large story drifts, whereas, the 2016 design method significantly improves the distribution of inelastic deformation along the height of the frame and prevents column instability. Furthermore, it was found that the 2016 AISC Seismic Provisions accurately estimate the axial load but overestimate the in-plane flexural demands and underestimates the out-of-plane flexural demand. Nonetheless, the overestimation of in-plane flexure demands results in acceptable strength capacity even though out-of-plane flexural demands is underestimated.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
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".