Modeling of the composite action in fully restrained beam‐to‐column connections: implications in the seismic design and collapse capacity of steel special moment frames
Bibliographic record
Abstract
SUMMARY This paper investigates the effect of the composite action on the seismic performance of steel special moment frames (SMFs) through collapse. A rational approach is first proposed to model the hysteretic behavior of fully restrained composite beam‐to‐column connections, with reduced beam sections. Using the proposed modeling recommendations, a system‐level analytical study is performed on archetype steel buildings that utilize perimeter steel SMFs, with different heights, designed in the West‐Coast of the USA. It is shown that in average, the composite action may enhance the seismic performance of steel SMFs. However, bottom story collapse mechanisms may be triggered leading to rapid deterioration of the global strength of steel SMFs. Because of composite action, excessive panel zone shear distortion is also observed in interior joints of steel SMFs designed with strong‐column/weak‐beam ratios larger than 1.0. It is demonstrated that when steel SMFs are designed with strong‐column/weak‐beam ratios larger than 1.5, (i) bottom story collapse mechanisms are typically avoided; (ii) a tolerable probability of collapse is achieved in a return period of 50 years; and (iii) controlled panel zone yielding is achieved while reducing the required number of welded doubler plates in interior beam‐to‐column joints. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".