Finite element modelling of shear critical glass fibre-reinforced polymer (GFRP) reinforced concrete beams
Bibliographic record
Abstract
Due to the different properties of GFRP bars, the available finite element packages for modelling shear critical GFRP reinforced members are questionable. This paper presents a three-dimensional (3D) nonlinear finite element analysis (FEA) model for shear critical glass fibre-reinforced polymer (GFRP) reinforced concrete beams. The beams were reinforced in longitudinal direction and there was no shear reinforcement. The FEA were carried out using concrete damage plasticity model in ABAQUS along with suitable constitutive model for concrete. Perfect bond was assumed between concrete and GFRP reinforcement. A generalized bi-linear tension stiffening model, based on the strain energy density, was used to model the contact between the concrete and GFRP bars. The FEA results were compared with the test results of GFRP reinforced beams. The robustness of the model was investigated for three different parameters: depth of beam, shear span to depth ratio, and concrete strength. The results obtained from FE analysis were analyzed for its load-deflection behaviour, crack patterns, ultimate loads; and the FE results were also compared with the test results. The comparison reveals that the model predicts the behaviour of shear critical GFRP reinforced concrete beams with reasonable degree of accuracy.Abbreviation: Glass Fibre Reinforced Polymer (GFRP)
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".