Seismic performance analysis of high-rise RC shear walls reinforced with superelastic shape memory alloys
Bibliographic record
Abstract
The seismic performance of hybrid Shape Memory Alloy (SMA)-steel concrete shear walls containing Nickel-Titanium (Ni-Ti) superelastic SMA as alternative reinforcement in the plastic hinge zone is investigated. This wall system permits self-centering with high levels of energy dissipation and significant reduction of permanent deformations. A ductile type of Reinforced Concrete (RC) shear wall was investigated for a prototype 10-storey office building in the seismic design scenario of western Canada. The wall was designed according to the current Canadian design standards as conventional deformed steel-reinforced concrete shear wall. The resulting cross-section was used to define the geometry and reinforcement layout of equivalent hybrid SMA-steel RC wall. Full-scale 2-D Finite Element (FE) models of the walls were developed and subjected to nonlinear reverse cyclic analyses. Similarities in cross-section permitted a reliable comparison and assessment of the post-loading condition, including displacement capacity and drift, damage, residual displacement, and energy dissipation. The observed response of the hybrid SMA-steel wall, when compared to that of the steel-reinforced wall, indicated similar lateral capacity, slightly lower energy dissipation, and superior restoring capacity. In general, the introduction of Ni-Ti bars in the plastic hinge region of shear walls showed potential to optimize the seismic performance of reinforced concrete buildings, controlling residual deformations and thereby reducing damage to 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".