Numerical Simulation of the Pullout Behavior of Steel Fiber Composite Bars (SFCB) Embedded in Concrete
Bibliographic record
Abstract
Steel fiber composite bars (SFCB) are a promising alternative to steel and fiber-reinforced polymer (FRP) reinforcement due to their high elastic modulus and tensile strength, impressive ductility, and outstanding corrosion resistance.This research investigates the impact of increasing the diameter of the inner steel bar of a glass SFCB on the bond stress between the bar and the surrounding concrete.Abaqus finite element software simulates the pullout behavior between glass SFCB and normal-strength concrete.Results of the FE models were compared with experimental results in terms of crack patterns and bond stresses.The FE model results correlated well with the experimental results having a 0.54% error in the ultimate bond stress and a 0.67% error in the corresponding slip.A parametric study is conducted in which the diameter of the inner steel core is increased while all other material properties and boundary conditions remain constant.The composite material's behavior is analyzed accounting for the interactions between the steel bar, glass FRP (GFRP) cover, and surrounding concrete.Notably, increasing the diameter of the inner steel bar from 8.6 mm to 12.6 mm increases the bond stress from 20.7 MPa to 25.1 MPa and increases the ultimate slip from 1.17 mm to 1.57 mm.The increase in ultimate slip is due to the rise in bond stress which in turn is due to a decrease in the radial confinement stress between the SFCB and the surrounding concrete.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".