Data-Fitted Shear Design Equations for EB FRP-RC Beams
Bibliographic record
Abstract
Based on a data fitting method applied to 490 experimental test data that are publicly available in the literature, this study provides simplistic and straightforward equations to determine the shear capacity of FRP bonded-RC beams.Complete wrap, U-wrap, and side wrap schemes pertaining to Carbon Fiber Reinforced Polymer (CFRP) were analysed separately.Current design codes follow a customary approach where the nominal shear capacity is calculated by simply accumulating the shear contribution of concrete, transverse reinforcement, and FRP.The interaction between concrete, transverse reinforcement, and FRP is usually not taken into consideration.While the modulus of elasticity of FRP, transverse steel ratio, and FRP ratio all have an inverse interaction with the effective strain of FRP, the concrete compressive strength, longitudinal steel ratio, and the shear span-to-depth ratio are positively linked with the effective FRP strain.This investigation further showed that as transverse reinforcement is increased, the influence of FRP on shear contribution significantly decreases in the complete wrap scheme.ACI 440.2R-17,CSA S806-02 and its latest version CSA S806-12 are among the regularly used shear design codes in North America and they were used to compare the performance of the proposed equations.The obtained results show that the proposed equations predict the experimental results more accurately than ACI 440.2R-17,CSA S806-02, and CSA S806-12.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".