Mechanical Model for Non Ductile Reinforced Concrete Columns
Bibliographic record
Abstract
Column failure is the primary cause of collapse during earthquakes for many existing reinforced concrete buildings. The main objective of this article is to develop a macro model to reproduce the lateral load–deformation response of reinforced concrete columns with limited ductility due to degradation of shear resistance. This model will eventually be used to perform probabilistic assessments of collapse risk for existing reinforced concrete frame structures, and hence must be computationally efficient. Current modeling approaches for reinforced concrete components provide a reasonably accurate prediction of flexural and longitudinal bar slip response, while shear deformations and, in particular, post-peak shear behavior needs further development. The shear response introduced in this article is based on a mechanical approach for pre-peak, point of shear failure, and post-peak behavior of reinforced concrete columns. Flexural, shear, and longitudinal bar slip responses are simulated by individual springs in series. These springs are combined to obtain the total lateral response of the column. The column model has been implemented in OpenSees and validated using data from column tests representing a broad range of design parameters typical of older reinforced concrete frames.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".