Effects of low cycle fatigue and inelastic buckling on the superelasticity and energy dissipation capacity of NiTi SMA rebar
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".