A Simplified Axial-Shear-Flexure Interaction Approach for Load and Displacement Capacity of Reinforced Concrete Columns
Bibliographic record
Abstract
A simple performance-based evaluation approach is presented based on interactions of axial, shear, and flexure mechanisms to estimate the axial and lateral deformation and load capacities of reinforced concrete columns. The developed model is based on a simplification of a consistent but relatively more complex approach known as the axial-shear-flexure interaction (ASFI) method, which is able to predict the full load-deformation response of reinforced concrete columns subjected to axial, flexure and shear force. The analytical model was developed by coupling an axial-shear model, which is a biaxial shear model, and an axial-flexure model, which is the traditional section analysis. Axial deformation interaction is the main compatibility condition taken into account in this approach. Equilibrium conditions are satisfied through a simple shear and flexure stress relation. A series of reinforced concrete columns subjected to axial and lateral loads, tested by various researchers, were evaluated by the simple developed approach and the results were compared with the test data; consistent correlation and agreement were achieved. This paper describes the formulation, implementation and verification of the modified approach. A future attempt is to modify the ASFI method for response estimation of reinforced concrete columns in fire under axial load and lateral deformation induced by thermal expansion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".