Evaluation of Shear Capacity of FRP-Reinforced Concrete Beams
Bibliographic record
Abstract
Existing methods for calculation of shear capacity of concrete beams reinforced with fiber-reinforced polymer (FRP) are generally based on slightly modified versions of well established semi-empirical shear design equations. These equations were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and consequently exhibit different modes of failure than steel, making the extension of existing shear design equations for steel-reinforced concrete (S-RC) beams to cover concrete beams reinforced with FRP somehow inaccurate. Current available methods include ACI 440-06, JSCE-97, CSA S806-02, and ISIS Canada-01. Availability of FRP reinforcement products varies in terms of capacity and modulus of elasticity, which can result in a significant change in behavior. An experimental database of 150 FRP-reinforced concrete (FRP-RC) beams was collected from published literature. Subsequently, this database was used to assess the validity of these four main existing shear design methods for FRP-RC beams. This study investigates the performance of the abovementioned design methods to estimate the nominal shear capacity, Vn of steel-free concrete beams reinforced with FRP bars. Results show that current design guidelines provide a shear strength underestimation in the case of beams without shear reinforcement and a shear strength overestimation for beams with shear reinforcement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| 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 | 0.001 |
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".