Enhanced Anchorage Techniques for Smooth Surfaced Nitinol-SMA Rebars
Bibliographic record
Abstract
Over the past decades, Shape Memory Alloys (SMAs), have revolutionized the field of seismic and structural engineering, offering their unprecedented unique properties such as superelasticity, energy dissipation, and the ability to undergo remarkable deformations and reverting to their original shape. The origins of SMA date back to the 1930s when Swedish scientist Arne Ölander initiated revolutionary research on iron alloys, exploring the distinctive characteristics of Iron-Manganese (Fe-Mn) alloy. Ever since, researchers have extensively investigated the mechanical properties of SMAs, leading to increasingly utilizing them in a wide variety of applications, including self-centering braces, structural elements, and systems frequently exposed to harsh working conditions, such as in regions susceptible to earthquakes and dynamic loading. However, a critical limitation has emerged, particularly those made of Nitinol (Nickel–Titanium), which possesses a smooth surface that makes it hard to implement in most structural elements, therefore anchorage systems are often required. Consequently, this smooth surface increases the possibility for slippage, therefore conventional methods to anchor steel reinforcement bars may not be applicable. A few recent studies have investigated the anchorage of SMA rebars, but there is still a big research gap. To fill this research gap, this paper presents an experimental test to evaluate the possible anchorage systems for smooth-surfaced Nitinol-SMA rebars. A total of 6 specimens were tested under uniaxial tensile loading reaching a maximum strain level up to 6%, utilizing the two different anchorage systems. The tests were conducted at a constant loading rate of 0.5 mm/min to evaluate the effectiveness of these anchorage systems. The findings show that both proposed anchorage systems are appropriate for high-deformation seismic zones since it preserved the nitinol bar to sustain up to 6% strain without showing any signs of slippage. These results provide vital insights for creating structural parts with SMA integration that are more dependable. This paper's key findings include ultimate tensile strength, force/displacement relationship, and stress/strain relationship under different constant strain. This paper highlights the need for a more thorough investigation of innovative anchorage systems suitable with SMA bars to pave the way for researchers to enable their wider application in more structural elements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".