Size Effect in FRP Shear-Strengthened RC Beams: Design Models versus Experimental Data
Bibliographic record
Abstract
Numerous studies on the size effect have been devoted to reinforced concrete (RC) beams. They have shown that increasing the beam size leads to a decrease in ultimate shear strength (stress) at failure. This is reflected in the design model of most current international codes and guidelines, where the size effect is taken into consideration by reducing concrete contribution to the shear resistance (force). In contrast, the size effect of RC beams strengthened with externally bonded (EB) fibre-reinforced polymer (FRP) is not fully documented, and very few experimental studies have been devoted to the phenomenon. The objective of this study was to evaluate the accuracy of the current code and guideline models in terms of the size effect on the EB-FRP contribution to shear resistance. To this end, a database of experimental findings on the size effect in EB-FRP-strengthened beams was built based on the reported literature, as well as our own experimental tests. The data were analysed and compared with the predictions of six current codes and design guidelines to assess their accuracy. Experimental results clearly revealed the presence of a size effect related to EB-FRP as well as the existence of interaction between internal stirrups and EB-CFRP. Based on analysis of the collected experimental test results, the study clearly revealed that the predictions of current codes and guidelines overestimate the contribution of EB-FRP systems to shear resistance. The size effect tends to exacerbate this overestimation as the effective depth (d) of the beams increases. Therefore, until the size effect for RC beams strengthened in shear with EB-FRP is captured by the prediction models, current codes and design guidelines are to be used with caution.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".