Torsional Performance of Reinforced Concrete Beams Strengthened with Ferrocement
Bibliographic record
Abstract
Reinforced concrete (RC) beams strengthened with ferrocement and exposed to pure torsion are the study's main focus.Ferrocement is utilized for strengthening because it's price-effective.The solution is cost-effective and structurally efficient.Ferrocement is cheaper, more accessible, and has good ductility, durability, and bond performance.Five RC beams were encased with 2.5 cm of ferrocement on each side and subjected to pure torsion for the investigation.The other two beams were references.Each strengthened beam had a cross-section of 100×250 mm and a constant length of 2000 mm.The first control beam without torsional reinforcement has the same reinforcement features as all enhanced beams.This study examined the effects of wrapping beams from three and four sides and the presence or absence of strengthening, plastic, and steel wire mesh layers.All specimens enhanced with the ferrocement layer had better RC beam torsional performance than control beams.Compared to reference beams (b1, b2), b4 (beam strengthened with four faces) had the best torsional moment resistance and the highest ultimate torque moment.Strengthened beams with three faces (U-wrapped) (b4, b5, b6, b7) and different steel and plastic wire mesh layers increase ultimate torque (233%, 233%, 221%, 209%) for b1 and (114%, 114%, 106%, 99%) for b2.
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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 | 0.000 |
| 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".