Shape Memory Alloys for Seismic Response Modification: A State‐of‐the‐Art Review
Bibliographic record
Abstract
Shape memory alloys (SMAs) are a remarkable class of metals that can offer high strength, large energy dissipation through hysteretic behavior, extraordinary strain capacity (up to 8%) with full shape recovery to zero residual strain, and a high resistance to corrosion and fatigue—aspects that are all desirable from an earthquake engineering perspective. Their various physical characteristics result from solid‐solid transformation between austenite and martensite phases of the alloy that may be induced by stress or temperature. The most commercially successful SMA is a binary alloy of nickel and titanium (NiTi). Although SMAs are expensive relative to most other materials used in seismic engineering, in certain forms their capacity for high energy loss per unit volume means that comparatively small quantities can be made to be especially effective, for example when used in wire form as part of a seismic bracing system. This state‐of‐the‐art paper presents current materials science aspects, material models, and mechanical behavior of SMAs relevant to seismic engineering, and examines the current state of design of SMA‐based seismic response modification devices and their use in buildings and bridges. SMA‐based devices offer promising advantages for development of next‐generation seismic protection systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".