Probabilistic alternate path analysis of steel moment-resisting frames
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it