Study and Comparison of the Performance of Steel Frames with BRB and SMA Bracing
Bibliographic record
Abstract
Designing steel structures with Concentric Braced Frame (CBF) lateral systems has been common in recent decades. This type of bracing has a quite unstable and complicated behavior in relatively intense earthquakes. This study tries to improve the seismic behavior of steel frames with CBF braces equipped with Shape Memory Alloy (SMA) and Buckling Restrained Braces (BRBs). In this manner, a multi-story building with inverted V chevron bracing was considered. Nonlinear time-history analyses have been performed using OpenSEES software. The dynamic responses of frames with SMA and BRB braces were compared. The results showed that the SMA and BRB braces provide energy dissipation in the nonlinear zone and can reduce maximum interstory drift. The comparison of those bracing systems revealed that implementing SMA in braces also led to a reduction in permanent displacement of the structures due to the elasticity property of the SMA bracing system. The energy dissipation of structures with the BRB system was higher than that of structures with the SMA bracing system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".