Research on the flexural behavior of polypropylene fiber reinforced concrete beams with hybrid reinforcement of GFRP and steel bars
Bibliographic record
Abstract
Abstract To study the flexural behavior of glass fiber (GFRP) bars and steel bars hybrid reinforced polypropylene fiber concrete (Hybrid-PFRC) beams, one GFRP-PFRC beam, one Steel-PFRC beam, and five Hybrid-PFRC beams were designed and fabricated. The effects of the different area ratio ( $${{A_{f} } \mathord{\left/ {\vphantom {{A_{f} } {A_{s} }}} \right. \kern-0pt} {A_{s} }}$$ A f / A s ) of GFRP to steel bars and polypropylene fiber (PP) volume fraction on the flexural behavior of Hybrid-PFRC beams were investigated through experiments. The research results indicated that the Hybrid-PFRC beams’ load–deflection curves exhibited trilinear characteristics with specimen cracking and steel bars yielding as turning points. As $${{A_{f} } \mathord{\left/ {\vphantom {{A_{f} } {A_{s} }}} \right. \kern-0pt} {A_{s} }}$$ A f / A s increased, the flexural bearing capacity of Hybrid-PFRC beams increased, the deflection decreased, the crack spacing and width decreased, and the ductility decreased. The addition of PP did not significantly improve the flexural bearing capacity and cracking moment of Hybrid-PFRC beams, but it greatly enhanced the ductility of the beam. Moreover, PP had good advantages in controlling crack propagation in the beam. The article also used the theoretical model to predict and analyze the flexural behavior of Hybrid-PFRC beams. When predicting the maximum crack width of Hybrid-PFRC beams, when PP is not added to the beam, the bonding coefficient k b should be greater than 1.4; When PP is added to the beam, it is recommended that the bonding coefficient k b should not exceed 1.4.
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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".