Parametric study on a resilient hybrid steel-shape memory alloy yielding damper
Bibliographic record
Abstract
A critical factor obstructing structural resilience is residual deformation after a damaging event. The search for a solution for removing residual deformation has led to the consideration of Shape Memory Alloys (SMAs) that can undergo large deformations and return to their original undeformed shape. This study focuses on an innovative hybrid yielding steel-SMA damper that uses monofilament wire loops. Because of numerous design parameters that affect the performance of this hybrid damper, this research aims to carry out a parametric study to optimize the damper’s performance. The study was conducted at the device level as well as a structural level. At the device level, the key design parameters, including SMA to steel ratio, max imposed strain, and the length of the elements, were evaluated. At the same time, the damper’s effect on the seismic performance of low, mid, and high-rise structures was investigated at the structural-level study. In the end, the results showed that using the damper in strong ground motions successfully reduced the maximum residual drift up to 71%, 97%, and 92% for low, mid, and high-rise structures, respectively. Therefore, it was concluded that the damper could add the self-centering ability to hysteretic dampers while maintaining satisfactory energy dissipation performance. Furthermore, some of the advantages claimed by the developers, including versatility in design and performance, adequate load resistance, and stable behavior, were also confirmed in this study.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".