Influence of Strut Geometry on the Size Effect of FRP Reinforced Simply Supported Deep Beams: A Theoretical Analysis
Bibliographic record
Abstract
The reduction in the shear strength accompanied to the increasing in the section depth is characterised as size effect, assuming that all parameters are kept constant. Such behaviour is controlled by many factors. The geometry of the element formed between the load and support points is one of those factors that need to be highlighted. Owing to that, this study aims to assess the impact of the strut geometry on the size effect from the strut and tie method (STM) point of view. As the strut geometry is represented by bearing plates and concrete cover, the current study has focused on those two parameters. Accordingly, two groups of specimens have been examined analytically using STM of the American, European and Canadian codes. In each group, three depths were used of 500, 100, and 1500 mm. The only differences between those two groups are dimensions of bearing plates (loading and supporting) and concrete cover. In the first group, the dimensions of bearing plates and concrete cover have been kept constant with 250 mm and 60 mm, respectively regardless of the section depth. In the second group, those two parameters have been proportioned with the section height to be 15% and 8% of section height, respectively. Furthermore, an experimental database of 25 deep beams reinforced with polymer bars has been compiled from the literature to evaluate the ability of the STM to consider the size effect. The results showed that STM does not consider the size effect. Additionally, the collected data confirmed that the STM of American and European codes overestimated the shear capacity, while the STM of Canadian code gave a conservative prediction, highlighting the need of suitable models for shear strength prediction of FRP reinforced deep elements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".