Consideration of Shear Behavior in Macromodeling of Deep Reinforced Concrete Members
Bibliographic record
Abstract
Deep members can exist in different forms in concrete structures, such as coupling beams, short columns, pile caps, and corbels. Because they are typically prone to shear failure, accurate calculation of the shear behavior in these members is vital. The existing analysis procedures for deep members are either computationally expensive or limited to the calculation of shear strength for design purposes. There is a great need for reliable macromodeling analysis tools that can evaluate safety and performance of structures at the system level while considering the nonlinear shear behavior of deep members in detail. This paper presents a shear plastic hinge model developed based on the beam-arch action mechanism for nonlinear analysis of deep RC members. The contribution of web concrete and transverse reinforcement (i.e., beam action) to the shear response is considered based on the modified compression field theory, while the contribution of the inclined concrete compression chord (i.e., arch action) is taken into account using the compatibility condition for shear deformations. The model is capable of calculating the shear force and shear deformation at different stages of the response while considering important nonlinear material effects in RC and interactions between internal force components. Through a comprehensive verification and parametric study, it is demonstrated that the model is able to accurately compute the shear behavior in deep RC beams and columns with various design variables. Last, the effectiveness of the proposed model for system-level analysis of structures is evaluated by modeling a multistory RC shear wall with coupling beams. The analysis results show that shear deformations can have a great influence on the performance of the structure as a whole in addition to the behavior at the component level.
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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 | 0.001 |
| 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) | 0.001 | 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".