A post-tensioned self-centering yielding brace system: development and performance-based seismic analysis
Bibliographic record
Abstract
Recent seismic events demonstrated that excessive post-earthquake residual drifts might result in high repair costs, temporary interruption of building functions due to required repairs, or even in the total destruction of the buildings. As such, minimizing residual drifts is a critical step in facilitating the rehabilitation of a building after an earthquake. To this end, a highly resilient post-tensioned self-centering yielding brace system (PT-SCYBS) has been introduced and studied. PT-SCYBS is envisioned to be implemented in new or existing diagonally braced frame systems. This system relies on the inherent self-centering behaviour of post-tensioned wires along with the yielding behaviour of the steel bars to provide a flag-shaped hysteretic response. This paper presents a comparative study of the PT-SCYBS frames, moment-resisting frames (MRFs), and buckling restrained braced frames (BRBFs) using the results from a series of nonlinear dynamic analyses. Then, a comprehensive comparative seismic performance assessment of the PT-SCYBS buildings and the reference MRF and BRBF buildings has been conducted using the FEMA P58 methodology implemented in the PACT software. The results show that the PT-SCYBSs have lower residual drifts than the reference buildings, resulting in significant reductions in the repair costs/time and casualties, and exhibit improved seismic performance.
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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) | 0.000 | 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 | 0.001 | 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".