Superelastic Cyclic Properties of Cu-Al-Mn and Ni-Ti Shape MemoryAlloys for Seismic Mitigation
Bibliographic record
Abstract
Shape memory alloys (SMAs) have been getting much attention by many researchers in a variety of application areas due to their unique properties of superelasticity (SE) and shape memory effect (SME). They have the ability to recover large inelastic deformations upon heating (SME) and stress removal (SE). In recent years, structural engineers have been dealing with these smart materials to incorporate into civil engineering applications such as rebar in the reinforcement of concrete structures, repairing, retrofitting, base isolation system, dampers for vibrational control, etc. To overcome and mitigate the possible seismic risk of the structure under consideration, understanding the material characteristics of SMAs under various loading conditions is one of the critical steps. In this study, the mechanical properties of two popular SE SMAs, i.e. copper-aluminum-manganese (Cu-Al-Mn) and nickel-titanium (Ni-Ti), were investigated in detail. Moreover, the mechanical properties of the conventional rebar steel were also identified for comparison purposes. Room temperature monotonic and incremental cyclic tests were applied on dog-bone shaped Steel, Cu-Al-Mn and Ni-Ti tensile coupon specimens the obtain and compare their mechanical characteristics. The results showed that Cu-Al-Mn and Ni-Ti materials exhibited a significant re-centering ability upon unloading with negligible and comparable residual deformations whereas the Steel experienced higher permanent plastic deformations with almost 3% recovery at the same amount of deformation. In addition, the decrease in the amount of dissipated energy for Cu-Al-Mn and Ni-Ti for consecutive cyclic motion is much less than conventional steel. Test results were also evaluated in terms of cyclic performance of materials, residual strain, recovery capacity, dissipated energy and equivalent viscous damping. Experimental outcomes highlighted the potential usage of SMAs in seismic applications and supply basis information for continued research.
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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 | 0.000 | 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".