Experimental cyclic plasticity characterization of iron-based shape memory alloys for seismic applications
Bibliographic record
Abstract
This paper presents experimental findings on the inelastic cyclic behaviour of iron-based shape memory alloys (Fe-SMAs), focusing on their potential use in seismic applications. The research employed loading protocols that replicated the strain amplitudes, ranges, and rates, typically observed during earthquake loading conditions, to examine the primary characteristics of rate-independent and rate-dependent cyclic plasticity. It is shown that the material demonstrates a stable and symmetrical stress-strain hysteresis behaviour. At quasi-static loading rates, the material exhibits a strong resemblance to mild steel in terms of kinematic and cyclic hardening. Specifically, a stable hysteresis loop can be achieved within a few cycles of straining, and isotropic hardening depends mainly on the accumulated plastic strain. At higher loading rates, viscoplasticity and thermal effects influence the hardening response. It is shown that seismic-induced straining could cause surface temperatures to exceed 200°C. The impact of post-test thermal activation on the cyclic behaviour is also investigated. The study is further extended to the fabrication and testing of energy dissipaters using multi-fuse and grooved Fe-SMA cores encased in stainless steel tubes, demonstrating stable hysteresis response prior to buckling, and a minimum cyclic average strain capacity of 10 %.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".