Investigation of Low-Cycle-Fatigue Behavior of NiTi SMA Rebar and Development of Low-Cycle-Fatigue Model
Bibliographic record
Abstract
Shape memory alloys (SMAs) are smart metallic alloys that have become an attractive material for structural engineering applications owing to two distinct features: the superelasticity effect (SE), and the shape memory effect (SME). In RC structures, NiTi SMA reinforcing rebars have emerged as a suitable alternative for conventional steel rebars due to their ability to dissipate seismic energy and reduce earthquake-induced damage. Seismic application of NiTi SMAs in RC structures warrants investigating the low-cycle-fatigue (LCF) behavior of NiTi SMA bars. Furthermore, longitudinal rebar buckling in a RC column subjected to seismic loading is a common failure mode, and can accelerate the LCF failure of reinforcing rebar. However, there is a lack of research examining the LCF response of NiTi SMA rebar subjected to cyclic tension-compression loading, considering the buckling effects. To address this gap, this paper focuses on the LCF behavior of NiTi SMA rebars under cyclic tension–compression loading, and proposes LCF life prediction models considering the effects of buckling. Using numerical parametric analysis, various strengths, diameters (10, 12, and 15 mm), and slenderness ratios (5, 7, and 10) of NiTi SMA rebars were examined under different constant strain amplitudes (2%, 4%, 6%, 8% and 10%). The study incorporated the effects of buckling, and proposed total strain amplitude–based and dissipated energy–based LCF models to estimate the LCF life of NiTi SMA rebar. The comparison of the predicted LCF life and the results from numerical investigation validated the accuracy of the proposed models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".