Seismic performance of ductile corrosion-free reinforced concrete frames
Bibliographic record
Abstract
Corrosion of steel bars is the main cause of the deterioration of reinforced concrete (RC) structures. To avoid this problem, steel rebars can be replaced with glass-fiber-reinforced-polymer (GFRP). However, the brittle behaviour of GFRP RC elements has limited their use in many applications. The use of shape memory alloy (SMA) and/or stainless steel (SS) rebars can solve this problem, because of their ductile behaviour and corrosion resistance. However, their high price is a major obstacle. To address issues of ductility, corrosion, and cost, this paper examines the hybrid use of GFRP, SS, and SMA in RC frames. The use of SMA provides an additional advantage as it reduces seismic residual deformations. Three frames were designed. A steel RC frame, SS-GFRP RC frame, and SMA-SS-GFRP RC frame. The design criteria for the two GFRP RC frames followed previous research by the authors, which aimed at having approximately equal lateral resistance, stiffness, and ductility for GFRP and steel RC frames. The three frames were then analyzed using twenty seismic records. Their seismic performance confirmed the success of the adopted design methodology in achieving corrosion-free frames that provide adequate seismic performance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".