Optimal placement of post-tensioned self-centering yielding braced systems for braced frame structures
Bibliographic record
Abstract
The experience of past prominent earthquakes establishes the fact that the structure’s catastrophes and casualties can be dramatically decreased through the use of self-centering systems. A promising post-tensioned self-centering yielding braced system (PT-SCYBS) has been developed, comprising of two main components, including the post-tensioned wires, exhibiting the desirable self-centering properties, and steel bars, providing the energy dissipation capacity. The structural application of such systems is expeditiously expanding due to their capabilities of not only reducing the residual deformations of the structures but also improving the structure’s performance level. As such, identifying optimal design and proper placement of the proposed device in the structure is of crucial importance. In this paper, the mechanics of the proposed system, as well as a simple and efficient approach for determining the optimal design of the PT-SCYBS, have been proposed. Numerical models have been employed to examine the effect of various configurations of the device on the hysteretic behavior of the proposed PT-SCYBS. Nonlinear static and dynamic analyses are performed on the seismically deficient 3- and 9-story moment resisting frames (MRFs), enhanced with the optimal placement of the PT-SCYBS. Comparing the results of the PT-SCYBS buildings and MRFs, it can be concluded that the residual drift decreased by 96% and 77% for the 3- and 9-story buildings, respectively. As such, the optimal design of the proposed system in the building causes notably lower residual drifts as compared with the MRF buildings, resulting in enhanced 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 | 0.000 | 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".