Estimating the Seismic Performance of CFRP-Retrofitted RC Beam-Column Connections Using Fiber-Section Analysis
Bibliographic record
Abstract
Over the past two decades, many experimental techniques have been developed to improve the efficiency of the externally-bonded fiber-reinforced polymers (FRPs) in order to improve the structural performance of reinforced concrete (RC) beam-column connections. Numerical analysis is also being used as a cost-effective tool to predict the experimental results and to further investigate the parameters that are beyond the scope and capacity of experimental tests. In this study, at first, a fiber-section modeling approach is developed for estimating the seismic behavior of RC beam-column connections before and after application of FRP retrofits. The accuracy of the analysis results were validated against a series of the available experimental data under both monotonic and cyclic loadings. It was pointed out that the proposed model can predict the strength and displacement of un-retrofitted and FRP-retrofitted RC beam-column connections up to the failure points. The verified model was then used to perform a parametric study pertaining to the effect of longitudinal reinforcement ratio on the efficiency of the adopted FRP retrofitting technique to improve the structural behavior of RC beam-column connections.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".