Evaluation of Reinforced Concrete Columns under Biaxial Bending
Bibliographic record
Abstract
Modeling by finite element method provides a ground for better perception of the behavior of reinforce columns and it is also useful in prediction of the behavior of these members without the experimental results. The performance improvement depends upon different parameters including the geometry of columns and configuration of reinforcement layers. In this study, slender reinforced columns are modeled under axial and biaxial bending loading with Carbon fiberreinforced polymer (CFRP) with different slender ratios using Abaqus software. The model is validated by the results of Bilchek et al., experiments. In this design, 30 concrete unstrengthened hoop columns with the diameter 100mm and heights 200,400,600,800,1000mm are made and reinforced with bidirectional CFRP composites. In each slenderness, a control sample (unstrengthened) and 5 reinforced specimens with different fiber configurations (hoop, longitudinal, angel and their combination) are tested under axial loading and biaxial bending to the ultimate failure. The results showed that these composites increased strength and ductility of specimens considerably. The results showed that in unstrengthened specimens, by increasing slenderness from 1 to 10, strength and ductility were reduced as 35, 65%, respectively. The results showed that the modeling using experimental data had good consistency. Based on the shortage of experimental data in slender columns at big scale, by performing similar studies, the existing problems can be eliminated.
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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) | 0.001 | 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".