Seismic Retrofit of Square RC Short Columns with Shear-Flexural Failure Mode via CFRP Composites Using Different Confinement Techniques
Bibliographic record
Abstract
Short columns undergo larger drift ratios during earthquakes compared to ordinary ones, making them vulnerable structural elements if they are unable to withstand the demanded drift ratio and shear force without significant deterioration. In order to ensure proper structural performance, deficient reinforced concrete (RC) short columns should be effectively retrofitted. Carbon-fiber-reinforced polymer (CFRP) composites can be utilized effectively to provide adequate shear capacity and confinement for RC short columns to have a desirable performance during an earthquake event. In this study, six RC short columns were subjected to constant axial and reversed cyclic lateral loads to compare the three different confinement techniques of full-wrapping (FW), corner strip-batten (CSB), and corner strip-wrap (CSW) in terms of their efficiency. Moreover, the effect of a novel configuration of fiber anchors was investigated in improving the behavior of such short columns. Results indicated that the performance of a typical RC short column improved significantly with the use of the CSB technique, because its ductility and dissipated energy increased by 125% and 800%, respectively. Furthermore, the application of fiber anchors was observed to further improve the overall performance of short columns confined through either the CSB or the FW techniques.
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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) | 0.000 | 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) | 0.001 | 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".