Comparative seismic fragility assessment of buckling restrained and self-centering (friction spring and SMA) braced frames
Bibliographic record
Abstract
Abstract In seismic regions, steel braced frames are one of the most commonly used seismic lateral force resisting systems for their reliable performance. This study presents a comparative seismic performance of different braced steel frames at their system levels. Three types of bracings for steel frames are investigated: Buckling Restrained Bracing (BRB), Superelastic Shape Memory Alloy (SMA) bar reinforced Piston Based Self Centering (named as PBSC) bracing, and Friction Spring Based Piston Bracing (named as SBPB). A methodology to evaluate the structural response of the building in a probabilistic framework is used. The procedure to estimate the probability of exceeding certain limit states conditioned on the ground motion intensity is applied to the structures. Emphasis is given to the estimation of the probability of exceedance of peak Interstory Drift Ratios (IDR). The peak interstory drift ratio provides a way to estimate the damage to structural components. For this purpose, four, six, eight, and twelve-story structures, designed with the three bracing types are used. A large number of Incremental Dynamic Analyses are performed to derive three-dimensional (3D) vulnerability functions that involve building heights and bracing types. This versatile 3D format enables the interpolation of results to arrive at the seismic fragilities of structures with different stories. The results show that the SBPB and PBSC frames outperformed the BRB frames in terms of damage probability.
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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 | 0.001 | 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".