Feasibility Study on the Effect of FRP Shear Reinforcements on the Behaviour of FRP-Reinforced Concrete Deep Beams
Bibliographic record
Abstract
Unlike steel reinforcements in concrete, Fiber Reinforced Polymer (FRP) materials are light and free from corrosion. Therefore, FRP materials are increasingly being used in structural engineering as a replacement for steel reinforcements. While the use of FRP bars as longitudinal reinforcements in concrete deep beams has been studied somewhat widely, their use and effectiveness as web reinforcements are not well studied. In this study, the effect of the FRP web reinforcements on the behaviour and strength of FRP-reinforced concrete deep beams were investigated in an experimental study. Four glass fiber-reinforced concrete (RC) simply supported deep beam specimens were tested under a concentrated load with different shear span-to-depth ratios and web reinforcement ratios. The behaviour of the deep beams was described in terms of load–deflection behaviour, crack developments, strain in FRP reinforcements, and failure modes. The experimental investigation emphasized the significance of web reinforcements in determining the reinforced concrete deep beam behaviour, such as mid-span deflection, crack breadth, failure modes, and ultimate strengths. Furthermore, to predict the behavior of deep beams, numerical Finite Element models using Abaqus software were created. The present test results were compared to those predicted using the Finite Element models. This investigation shows that web reinforcement is quite important for FRP-RC deep beams to achieve a robust behaviour by enhancing its capacity and deformability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".