Experimental study on recovery stress losses in Fe-SMA rebars under semi-cyclic loads considering different activation temperatures and multiple activations
Bibliographic record
Abstract
Iron-based Shape Memory Alloys (Fe-SMAs) offer a promising and cost-effective solution for strengthening existing concrete structures. When heated to temperatures of 160 °C or higher and subsequently cooled while properly anchored, Fe-SMA bars generate recovery stresses through the Shape Memory Effect (SME), allowing them to function as prestressing elements. However, recent research has shown that Fe-SMA rebars can experience a reduction in recovery stress when subjected to semi-cyclic loading. These loading conditions, which do not involve reversals between tension and compression, may reflect the effects of different types of live loads. This study presents an experimental campaign aimed at quantifying recovery stress losses under semi-cyclic loading. The results indicate that Fe-SMA bars are highly sensitive to semi-cyclic loading, with complete recovery stress loss occurring even at moderate strain levels. However, it was also observed that successive activations can restore the initial recovery stresses, highlighting the potential of multiple activations as a viable strategy for maintaining long-term performance under service conditions. • Iron-based SMA rebars of different diameters and manufacturing dates were characterized. • Recovery stresses generated at activation are lost under significant semi-cyclic loading. • Stress losses in Fe-SMA rebars may reduce their effectiveness in concrete strengthening. • Higher activation temperatures slightly reduce the magnitude of stress losses. • Multiple activations can help mitigate recovery stress losses in Fe-SMA rebars.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".