Finite Element Modelling of Shear Critical Concrete Beams Reinforced with Basalt Fibres and Basalt Bars
Bibliographic record
Abstract
Finite element (FE) models capable of simulating the nonlinear shear behaviour of concrete beams reinforced with basalt fibres (BF) and basalt fibre-reinforced polymer (BFRP) bars were developed.Experimental tests were carried out to verify the validity of the prediction of the FE models.A parametric study was then conducted to investigate the effectiveness of using BF at different volume fractions (vf) to upgrade the shear capacity of BFRP-reinforced concrete beams made with recycled concrete aggregates (RCA) with different replacement percentages.Published characterization test results were used as input data in the FE modelling.Results of the FE analysis indicated that beam models with RCA replacement percentages of 30-100% exhibited 10-28% reductions in the shear capacity compared with that of a control beam model made with natural aggregates (NA).The beam models with 30 and 60% RCA containing BF at vf = 0.5% exhibited a shear capacity comparable to or higher than that of the control beam model with NA.Despite the improvement in the shear response of the beam models with 100% RCA caused by the addition of BF, their shear capacity was lower than that of the control beam model with NA.The shear capacity of the beam model with 100% RCA containing BF at vf = 1.5% was 93% of that of the control beam model with NA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".