Shear Strengthening of RC Beams with FRP Composites: Database of FE Simulations and Analysis of Studied Parameters
Bibliographic record
Abstract
The use of externally bonded fiber-reinforced polymer (EB-FRP) composites for shear strengthening of reinforced concrete (RC) beams presents many challenges given the complex phenomena that come into play. Premature bond failure, the behavior of the interface layer between FRP composites and the concrete substrate, the complex and brittle nature of shear cracks, and the adverse interaction between internal steel stirrups and EB-FRP are some of these phenomena. Compared to experimental investigations, the finite element (FE) technique provides an accurate, cost-effective, and less time-consuming tool, enabling practicing engineers to perform efficient, accurate nonlinear and dynamic analysis as well as parametric studies on RC beams strengthened with EB-FRP. Since 1996, many numerical studies have been carried out on the response of RC beams strengthened using FRP. However, only a few have been related to RC beams strengthened in shear using EB-FRP composites. In addition, the analytical models that have been reported so far have failed to address and encompass all the factors affecting the contribution of EB-FRP to shear resistance because they have mostly been based on experimental studies with limited scopes. The aim of this paper is to build an extensive database of all the studies using finite element analysis (FEA) carried out on RC beams strengthened in shear with EB-FRP composites and to evaluate their strengths and weaknesses through various studied parameters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".