Superelastic shape memory alloy flag-shaped hysteresis model with sliding response from residual deformation: Experimental and numerical study
Bibliographic record
Abstract
Superelastic shape memory alloy exhibits flag-shaped hysteresis with self-centering capability. Nevertheless, shape memory alloy undergoes some residual deformation after large plastic strain, especially under repeated cyclic loading. In order to accurately simulate this behavior during nonlinear dynamic time-history analysis, a shape memory alloy flag-shaped hysteresis model with sliding response has been developed. This article shows the gradual development process of this new hysteresis model and provides analysis and verification results to support this claim. A MATLAB-based superelastic uniaxial shape memory alloy material hysteresis model has been developed and was incorporated into a finite element program specifically designed for the piston-based self-centering bracing. This piston-based self-centering bracing system uses superelastic shape memory alloy bars for its energy dissipation and self-centering capability. A proof-of-concept brace specimen was fabricated and tested where numerical and experimental results showed excellent matching. The finite element program was utilized to capture the varying nonlinear quasi-static response of the piston-based self-centering brace. Finally, the piston-based self-centering brace responses from this analysis were used to develop a novel shape memory alloy flag-shaped hysteresis model with sliding response, which was implemented in finite element analysis and design software, S-FRAME. Nonlinear dynamic time-history analysis proves the effectiveness of such bracing in steel frames in reducing interstory drift.
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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 | 0.001 | 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".