Probabilistic alternate path analysis of steel moment-resisting frames
Bibliographic record
Abstract
This research presents a probabilistic assessment of progressive collapse in intermediate Steel Moment-Resisting Frames (SMRFs) subjected to different levels of column damage. Damage levels were represented by gradual reductions in column stiffness to the extent that tensile forces are created in columns sustaining large deformations, and columns were allowed to enter completely plastic zone. To this aim, low- to mid-rise structures with 4, 8, and 12 stories were examined. The effect of composite slabs on the vertical displacement response of SMRFs was taken as a variable. A number of 11 column damage scenarios were defined with either one or two damaged columns, located at different positions in the plan, which were applied to all floor levels. Incremental dynamic analysis was conducted for each damage scenario using the finite element OpenSees framework. Moreover, a state-of-the-art approach was employed in fragility analysis. The results showed that higher floors are more sensitive to partial damage due to less structural components involved in progressive collapse. However, in case of large damages, lower floors prove to be more critical due to greater deal of gravity loads. Shorter and taller structures perform better in large and partial damages to column, respectively. Moreover, the failure probability of SMRFs reduced by considering the composite slab stiffness. Subjected to single-column damage scenarios, SMRFs reach life safety limit state once tensile forces are created in the damaged column. In contrast, all performance levels are met in double-column damages when the column has still its compressive capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".