Macro Modeling of Column Removal in RC Frames with Consideration of Importance Factor and Infill Walls
Bibliographic record
Abstract
In this study, the effect of importance factor (IF) on RC frames with and without infill walls, in both with and without opening conditions, is evaluated against progressive collapse. For this purpose, RC building with the intermediate moment frame system for three levels of importance factor that these levels are intermediate, high, and very high IF is designed. OpenSees program is utilized for modeling RC frames. For this aim, the accuracy of modeling of column removal and infill walls are compared with experimental researches. In the present study, nonlinear dynamic analysis (NDA) and push-down analysis (PDA) were used for evaluating RC frames against progressive collapse in each column removal scenario. Analysis results showed that the effect of the importance factors in NDA and PDA are reduced to less than 24% and 13% when the infill walls are modeled in the frames. In the frame without infill walls, the influence of the importance factor is increased up to 36.1%. Also, in this study, it was found that the role of importance factors depends on the place of the removed column, which the effect of middle column removal is relatively twice than the corner column removal due to more redundancy. Other results about infill walls effects and opening in infill walls are presented in the paper. Finally, a proposed approach for column removal in NDA via OpenSees program is introduced, and its high accuracy is shown. This developed algorithm can remove any element of structure in different time intervals.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".