Research developments of a novel self-prestressing system for flexural strengthening reinforced concrete structures
Bibliographic record
Abstract
The state of infrastructure in Canada is concerning, with many structures requiring rehabilitation or replacement in the near future. Researchers are investigating Fe-SMAs as a novel prestress strengthening system to address this need. The novel system provides a faster and more adaptable alternative to traditional methods. The straightforward and quicker installation system makes Fe-SMA feasible at any point in a structure's lifespan. The key to this system is its ability to self-prestress through the shape memory effect, developing recovery stress when restrained. This paper systematically reviews studies on the flexural strengthening of reinforced concrete (RC) beams using Fe-SMA, discussing the system’s performance, installation techniques (EB/EUB, NSM, CR), and long-term durability. It compares conventional prestress strengthening techniques and outlines key challenges and research directions. Findings suggest that Fe-SMA can effectively extend the service life of infrastructure by delaying deterioration while addressing the limitations of current methods. Fe-SMA offers advantages over traditional materials, such as steel and CFRP, including improved ductility, enhanced corrosion resistance, and cost-effectiveness. Its effectiveness has been demonstrated in real-life applications, such as the strengthening of a 113-year-old bridge, which showcases its potential for large-scale rehabilitation. Additionally, Fe-SMA offers environmental benefits, including ease of reintegration and the ability to be processed into new materials. The paper identifies challenges such as stress relaxation, non-uniform stress distribution during sequential activation, and the need for advanced anchorage systems. A notable feature is Fe-SMA's ability to recover stress losses through multiple activations, which requires standardized reactivation protocols. Future research should focus on quantitative cost and life-cycle analyses, as well as environmental impact assessments, to further validate the advantages of Fe-SMA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".