Development of Shear Plastic Hinge Models for Analysis of Reinforced Concrete Members
Bibliographic record
Abstract
The lumped plasticity analysis approach is one of the most efficient methods to calculate the nonlinear behavior of reinforced concrete (RC) structures.However, the number of models that can capture shear effects using this approach are limited, and the existing models mostly require iterations or calibration.This study presents three shear hinge models developed based on the Modified Compression Field Theory, applicable to RC beams and columns with various shear span-to-depth ratios.A set of closed-form equations is developed for each model to calculate the shear force and shear deformation of the member at key points of the structural response.The proposed plastic hinge models are verified against various experimental results and finite element models.Moreover, parametric studies are conducted to assess the application range of the models.It is shown that the proposed models can accurately capture the nonlinear response of shear-critical RC structures in a computationally efficient manner.I would like to express my deepest gratitude to my supervisor, Dr. Vahid Sadeghian, who always advised me throughout my research with his thoughtful comments and recommendations, helping me to improve in academic and personal aspects.I would also like to thank my mother, father, and sister for their support and spiritual enlightenment in the moments where I felt exhausted.We were far apart but our hearts were close.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".