Concrete Shear Strength of Normal and Lightweight Concrete Beams Reinforced with FRP Bars
Bibliographic record
Abstract
A new equation to predict the contribution of concrete to the shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams without shear reinforcement is proposed. The new equation considers the elastic modulus ratio of the FRP bars to the steel reinforcement, the shear span to depth ratio, and the flexural reinforcement ratio, and was developed using the results of 60 concrete beam tests. The proposed equation more accurately predicted the results of various experiments available in the literature than the equations of an American Concrete Institute standard, and yielded similar degrees to the equations of a Canadian Standards Association standard, despite making somewhat higher predictions. The applicability of the proposed equation to FRP reinforced lightweight concrete beams was investigated using 24 all-lightweight concrete beam tests. The concrete shear strengths of the FRP reinforced all-lightweight concrete beams were equivalent to 75% of the strengths predicted by the proposed equation for normal concrete. Furthermore, with a reduction factor of 0.85, the proposed equation also showed good results for the concrete shear strength of glass FRP (GFRP) reinforced sand-lightweight concrete panels presented in a recently published paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".