Effectiveness of FRCM System in Strengthening Reinforced Concrete Beams
Bibliographic record
Abstract
In this paper, experimental work has been reported to investigate the efficiency of fiber-reinforced cementitious matrix (FRCM) in enhancing the flexural capacity and deformational characteristics of reinforced concrete (RC) beams.The aim of the experimental work is to assess the parameters that contribute to such enhancement.Twelve RC beam specimens, 2500 mm long, 150 mm wide and 260 mm deep, were prepared with two different reinforcement ratios of: ρ s D12 = 0.72% and 𝜌 𝑠 D16 =1.27% , representing under-reinforced beam sections.The strengthened beams utilized two FRCM types; namely carbon and polyparaphenylene benzobisoxazole (PBO) FRCM systems.The RC beam specimens were tested in flexure under four-point loading until failure.Two beams without FRCM strengthening were used as control specimens.Six beams were externally reinforced by one, two and three layers of carbon FRCM system.Four beams were strengthened with one and two layers of PBO FRCM system.From the experimental observations, a reasonable gain in flexural strength was achieved for both the FRCM systems.Results showed that the flexural capacity of carbon FRCM strengthened beams (FRCM stiffness = 1422 MPa) can be increased by 78% and of PBO FRCM counterparts (stiffness = 605 MPa) by 27.5% over that of their control (un-strengthened) specimens.
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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".