Seismic resilience of RC structures with shape memory alloys: Past and new perspectives
Bibliographic record
Abstract
This review comprehensively investigates the role of shape memory alloys (SMAs) in enhancing the seismic resilience of reinforced concrete (RC) structures. Emphasizing the unique properties of SMAs, superelasticity (SE) and the shape memory effect (SME), the study classifies their applications across various RC elements, including beams, columns, shear walls, and beam-column joints. The review synthesizes findings from more than 100 experimental studies, detailing both internal and external SMA deployments, and highlights the performance metrics most relevant to seismic design: residual drift, energy dissipation, stiffness degradation, and load-carrying capacity. Distinct comparisons are drawn between Ni-Ti, Cu-based, and Fe-based SMAs, offering clarity on their context-specific advantages. A significant contribution of the study is the structured evaluation of hybrid systems that integrate SMAs with supplementary materials such as ECC, UHPC, and FRP, which demonstrate enhanced composite action and address SMA limitations such as low damping. The review identifies critical gaps in long-term performance data, implementation feasibility, and design standardization, and offers forward-looking recommendations for multi-material hybrid configurations, coupler development, and codification pathways. This work offers a consolidated reference point for researchers and practitioners seeking to advance the practical development of SMA-based solutions toward broader structural applications in seismic regions. By addressing key research gaps, the study advances the understanding of SMA applications in seismic engineering and outlines future directions for achieving resilient and sustainable RC structures. • Detailed review of SMA applications in RC structures for construction, retrofitting, and repair. • Seismic performance evaluation: energy dissipation, residual displacement, and self-centering. • Exploration of hybrid SMA-supplementary materials for optimized seismic resilience. • Identification of research gaps and recommendations for future SMA applications. • Practical insights for integrating SMAs into earthquake-resistant RC designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".