Computational Approach for Collapse Assessment of Concentrically Braced Frames in Seismic Regions
Bibliographic record
Abstract
This paper proposes a computational approach for the collapse assessment of concentrically braced frames (CBFs) subjected to earthquakes. Empirical formulations for modeling the postbuckling behavior and fracture of three main steel brace shapes that are commonly used in CBFs are developed. These formulations are based on extensive calibrations of a fiber-based steel brace model with available information from a recently developed steel brace database. As part of the same computational approach, the representation of strength and stiffness deterioration associated with plastic hinging in steel columns and gusset-plate beam-to-column connections is considered. Through a case study of a 12-story Special Concentrically Braced Frame (SCBF), the influence of classical damping on the collapse capacity of CBFs is investigated. It is demonstrated that when SCBFs attain a negative stiffness during an earthquake, their collapse capacity can be significantly overestimated, if viscous damping is based on a commonly employed Rayleigh assumption with initial stiffness approximation. It is shown that sidesway collapse of CBFs should be traced based on a combination of criteria that associate large story drift ratios and the story shear resistance of a CBF at the corresponding story drift ratios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".