Modelling of Hybrid Steel-SMA Shear Walls with Local Strain and Reinforcement Considerations
Bibliographic record
Abstract
The incorporation of Super-elastic (SE) Shape Memory Alloys (SMAs), such as Nitinol (Nickel-Titanium), presents a method of passively improving the damage resiliency of traditional reinforced concrete structural elements. This resiliency can be of particular use in seismic applications owing to the super-elastic behavior allowing for increased recentering. Currently available experimental data on hybrid slender shear walls incorporating both steel and SE-SMAs has demonstrated the recentering improvements while also highlighting that use of SE-SMAs tend to cause the formation of a predominant crack that controls the response. The ability to more accurately predict this behaviour through numerical modelling would allow for better understanding of the salient parameters affecting the response of hybrid slender shear walls. The paper herein presents a numerical modelling methodology that considered the impacts of local strain concentration as a result of a singular predominant crack formation during reverse-cyclic loading. This modelling was performed in VecTor2, a non-linear two-dimensional finite element program, in conjecture with experimental data of a hybrid steel-SMA slender shear wall and a steel-only reinforced concrete companion wall. The use of a tension stiffening constitutive model which considered local fracture was found to improve the displacement prediction capabilities of models. Additional considerations which were investigated included local reinforcement properties to reflect conditions at the wall base and accounting for the impact of strain gauge instrumentation on the location of rupture of the reinforcement. The results provide insight into the numerical modeling that captures the local strain and reinforcement conditions present in hybrid steel-SMA and reinforced concrete slender shear walls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".