Seismic Design Criteria for Steel Moment Resisting Frames for Collapse Risk Mitigation
Bibliographic record
Abstract
This paper quantifies the lateral overstrength and the collapse risk of steel buildings with perimeter special moment frames (SMFs) designed in highly seismic regions in North America. State-of-the-art analytical models that consider the contributions of the composite concrete slab and the interior gravity framing to the lateral resistance and strength of steel frame buildings are employed. The findings demonstrate that the quantification of system overstrength based on dynamic analysis is more appropriate to nonlinear static analysis, since dynamic amplification of story shear forces due to higher mode effects is considered. Collapse risk is quantified using the mean annual frequency of collapse. It is found that low to mid-rise SMFs designed with a strong column weak-beam (SCWB) ratio larger than 1.0, achieve a probability of collapse larger than 1 percent in 50 years. A tolerable probability of collapse is achieved when a SCWB > 1.5 is implemented into the seismic design of steel SMFs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".