Thermo-mechanical characterisation of NiTi-based shape memory alloy wires for civil engineering applications
Bibliographic record
Abstract
Shape Memory Alloys (SMAs) exhibit a complex material behaviour due to thermo-mechanical coupling. At present, the understanding of their material behaviour under different thermo-mechanical loading conditions relevant to civil engineering applications is lacking even though they have been widely used in the past decade or so, for example mechanical behaviour at constant stress but variable temperature loading has not received much attention in civil engineering. In this study a comparative analysis of the mechanical behaviour of various NiTi-based SMAs is carried out to investigate: (1) stress-elongation and stress-temperature response under direct tensile loading; (2) thermo-mechanical behaviour under constant stress condition but under variable temperature loading (to investigate their ability to exhibit shape memory effect, if any, while carrying load); and (3) the ability to develop and retain recovery stress at room and sub-zero temperatures. Essentially, all these tests aim at investigating the potential/limitations of the SMAs in civil engineering in particular re-centring and heat-activated prestressing applications. The results from this study showed that at a constant stress of 600 MPa a large forward transformation strain, in the range of 5%–11%, is produced in NiTi-based SMAs when the temperature is lowered to or below the room temperature. This material behaviour of NiTi-based SMAs could be detrimental in many civil engineering applications of NiTi-based SMAs. However, all NiTi-based SMAs used in this study were found to have a good potential for re-centring applications, but only austenitic NiTi-based SMAs were found suitable for long-term prestressing applications. The maximum recovery stress developed on heating with constrained boundary conditions ranged between 230–750 MPa.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".