Utilizing shape memory alloys to enhance the performance and safety of civil infrastructure: a review
Bibliographic record
Abstract
Shape memory alloys (SMAs) are special materials with a substantial potential for various civil engineering applications. The novelty of such materials lies in their ability to undergo large deformations and return to their undeformed shape through stress removal (superelasticity) or heating (shape-memory effect). In particular, SMAs have distinct thermomechanical properties, including superelasticity, shape-memory effect, and hysteretic damping. These properties could be effectively utilized to substantially enhance the safety of various structures. Although the high cost of SMAs is still limiting their use, research investigating their production and processing is expected to make it more cost-competitive. Thus, it is expected that SMAs will emerge as an essential material in the construction industry. This paper examines the fundamental characteristics of SMAs, the constitutive material models of SMAs, and the factors influencing the engineering properties of SMAs. Some of the potential applications of SMAs are discussed, including the reinforcement and repair of structural elements, prestress applications, and the development of kernel components for seismic devices such as dampers and isolators. The paper synthesizes existing information on the properties of SMAs, presents it in concise and useful tables, and explains different alternatives for the application of SMAs, which should motivate researchers and practicing engineers to extend the use of SMAs in novel and emerging applications.Key words: shape memory alloy, superelasticity, shape-memory effect, construction, retrofitting.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".