Mechanical Model for Non Ductile Reinforced Concrete Columns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it