Effects of low cycle fatigue and inelastic buckling on the superelasticity and energy dissipation capacity of NiTi SMA rebar
Why this work is in the frame
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Bibliographic record
Abstract
Superelastic NiTi Shape Memory Alloy (SMA) rebars have emerged as compelling materials for structural engineering applications in concrete bridge piers, owing to their superior superelastic and energy dissipation properties. Incorporating NiTi SMA rebars enhances structural resilience against seismic loads by enabling effective earthquake energy dissipation while minimizing structural damage. However, under tension-compression cyclic loads, NiTi SMA rebars are subjected to strain reversals, leading to buckling and potential low cycle fatigue (LCF) failure. This study investigates the LCF behavior of NiTi SMA rebars under tension-compression cyclic loading, considering various strengths, diameters, and slenderness ratios ( L / D ). The findings indicate that NiTi SMA rebars with higher slenderness ratios experience accelerated LCF failure due to buckling, leading to deteriorated mechanical properties after fewer cycles compared to rebars with lower slenderness ratios. Moreover, the study reveals that total energy dissipation and residual strain of NiTi SMA rebars are influenced by strain amplitudes and slenderness ratios. Specifically, increasing the slenderness ratio and strain amplitude results in decreased total energy dissipation and increased residual strain, underscoring the significant impact of inelastic buckling on the LCF behavior of NiTi SMA rebars. Finally, equations are presented for the prediction of energy dissipation and residual strain of NiTi SMA rebars with different slenderness ratios under tension compression cyclic loading with different strain amplitudes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it