Failure progression resistance of a generic steel moment-resisting frame under beam-removal scenarios
Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the failure progression resistance of the steel moment-resisting frames subjected to various beam-removal scenarios after application of the design earthquake pertinent to the structure by investigating a generic eight-story building. Design/methodology/approach The structure is first pushed to arrive at a target roof displacement corresponding to life safety level of performance. To simulate the post-earthquake beam-removal scenario, one of the beam elements is suddenly removed from the structure at a number of different positions. The structural response is then evaluated by using nonlinear static and dynamic analyses. Findings The results show that while no failure is observed in all of the scenarios, the vulnerability of the upper stories is much greater than that of the lower stories. In the next step, the structural resistance to such scenarios is determined. The results confirm that for the case study structure, at most, the resistance to failure progression in upper stories is 58 percent more than that of lower stories. Originality/value Failure and fracture of beam-to-column connections resulting in removal of beam elements may lead to a chain of subsequent failures in other structural members and eventually lead to progressive collapse in some cases. Deficiency in design or construction process of structures when combined by application of seismic loads may lead to such an event.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".