Behaviour of Superelastic Nickel Titanium Shape Memory Alloy Material under Uniaxial Testing and its Potential in Civil Engineering
Bibliographic record
Abstract
Smart structures are defined as structures that able to adapt and maintain structural characteristics in dealing with changes of external disturbance, environment and unexpected severe loadings. This ability will lead to improve structural safety, serviceability and structural life extension. Shape memory alloys is one of the smart materials which has potential to be integrated in structural system to provide functions such as sensing, actuation, self-adapting and healing of the structures. The unique characteristic of shape memory alloys material is the ability to ‘remember’ its original shape after deformation. Nickel Titanium superelastic shape memory alloy wire is popular and widely used in many engineering fields and owned fully recovery of maximum strain of 6%-13.5% which is among the best shape recovery limit in alloy materials. The austenite finish temperature plays important role in stress-strain behaviour of superelastic shape memory alloys where higher stress required to complete martensite transformation with the increase of austenite finish temperature. The similar behaviour also is observed in the case of higher strain rate. The behaviour of superelastic shape memory alloys need to be studied before implementing in the structural system, so the targeted improvement for the structural system can be achieved.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".