Embedded Through-Section FRP Rod Method for Shear Strengthening of RC Beams: Performance and Comparison with Existing Techniques
Bibliographic record
Abstract
Embedded through-section (ETS) technique is a recently developed method to increase the shear capacity of reinforced concrete (RC) using fiber-reinforced polymer (FRP) rods. The ETS method presents many advantages over existing methods, such as externally bonded FRP sheets (EB FRP) and near-surface mounted FRP rods (NSM FRP). Unlike EB and NSM methods where the FRP relies on the concrete cover of RC beams, in the ETS method, the FRP relies on the concrete core of the RC beam, which offers a greater confinement and hence improves bonding performance. Additionally, the ETS method requires less concrete preparation compared with EB and NSM methods. The objective of this paper is to present results of an experimental investigation that studies the effectiveness of the ETS method and compares the performance of the ETS method with both EB and NSM methods. In total, 12 tests are performed on 4,520-mm-long T-beams. The parameters investigated are as follows: (1) the effectiveness of the ETS method, compared with EB FRP sheet and NSM FRP rod methods; (2) the presence of the internal steel; and (3) the internal transverse steel reinforcement ratio (i.e., spacing). The test results confirm the feasibility of the ETS method and reveal that the performance of the beams strengthened in shear using this method is significantly superior compared with that of the beams strengthened with EB and NSM methods.
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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 | 0.001 |
| 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 | 0.000 | 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".