MODELLING OF RC BEAM-COLUMN JOINTS AND STRUCTURAL WALLS
Bibliographic record
Abstract
The deformation of beam-column joints may contribute significantly to drift of reinforced concrete (RC) frames. In addition, failure may occur in the joints due to cumulative concrete crushing from applied beam and column moments, bond slip of embedded bars or shear failure as in the case of existing frames with nonductile detailing. When subjected to earthquake loading, failure in RC structural wall is similar to failure of frame joints as it may occur due to cumulative crushing from high flexural stresses, bond slip failure of lap splice, shear failure or a combination of various mechanisms of failure. It is important to include these behavioural characteristics in a simple model that can be used in the analysis of RC frames and RC walls to predict their response under earthquake loading and determine their failure modes. Global macro models for the beam-column joint and for RC structural walls are developed. The proposed models represent shear and bond slip deformations as well as flexural deformations in the plastic hinge regions. The models are capable of idealising the potential failure mechanism due to crushing of concrete, bond slip or shear with allowance for the simultaneous progress in each mode. The model predictions are compared with available experimental data and good correlation is observed between analytical results and the test measurements.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".