Seismic Performance Assessment of Multitiered Steel Buckling-Restrained Braced Frames Designed to 2010 and 2022 AISC Seismic Provisions
Bibliographic record
Abstract
This paper aims to evaluate the seismic response of multitiered buckling-restrained braced frames (MT-BRBFs), assess the design provisions specified by the 2022 AISC Seismic Provisions for multitiered BRBFs, and propose improvements to these provisions. A set of 17 frames is first selected by varying bracing configuration, frame height, number of tiers, and tier height ratio. The frames are then designed in accordance with the 2010 and 2022 AISC Seismic Provisions. A numerical parametric study is performed under scaled ground motion accelerations. The results of the parametric study show that when the frames are designed to the 2010 provisions, the frame inelastic deformation tends to concentrate in the tier(s) undergoing tensile yielding due to their lower postyield stiffness, compared to BRBs yielding in compression, which creates unequal story shear, contributed by braces, in adjacent tiers with BRBs in tension and compression and engages column flexure to compensate for unbalanced brace story shears between tiers. Columns experience yielding and even buckling in several cases due to combined flexural and axial load demands. MT-BRBFs designed to the 2022 AISC Seismic Provisions exhibit a more uniform deformation response between tiers and relatively lower flexural demands in their columns. However, these provisions may overestimate column in-plane flexural demands (on the order of 3), resulting in potentially uneconomical design solutions. On the basis of the numerical simulations, modifications are proposed to compression BRBs’ adjusted strength to better estimate column in-plane flexure and tier deformation while achieving an economical column design. The proposed improvements are validated using dynamic analyses.
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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) | 0.001 | 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 | 0.000 | 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".