Finite element analysis of ductility and flexural capacity of <scp>FRP</scp> and steel bars hybrid reinforced concrete beams
Bibliographic record
Abstract
Abstract To study the flexural behavior of hybrid reinforced concrete (hybrid‐RC) beams with FRP and steel bars, this paper used ABAQUS finite element (FE) software to model and nonlinear analyze the hybrid‐RC beams and investigate the effects of effective reinforcement ratio, concrete strength, and FRP bars type on the flexural behavior of hybrid‐RC beams. In addition, the FRP bars stress, flexural bearing capacity, and ductility formulas for hybrid‐RC beams were fitted based on the FE results, and existing literature data were used to verify their accuracy. The FE model results indicated that the effective reinforcement ratio, FRP bars type, and concrete strength significantly impacted the flexural bearing capacity and deformation performance of hybrid‐RC beams. Increasing the strength of concrete improved the flexural bearing capacity and ductility of hybrid‐RC beams; increasing the effective reinforcement ratio and the elastic modulus of FRP bars increased the flexural bearing capacity of the hybrid‐RC beams but decreased its ductility. In addition, the fitted FRP bars stress, flexural bearing capacity, and ductility calculation formulas can quickly and conveniently predict the flexural bearing capacity and ductility of hybrid‐RC beams.
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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 | 0.001 | 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".