Robustness and Retrofit Strategies for Seismically-Designed Multistory Steel Frame Buildings Prone to Progressive Collapse
Bibliographic record
Abstract
Unlike seismic engineering that attracted the attention of researchers, designers, and code developers for decades, the phenomenon of progressive collapse of structures still needs considerable amount of investigation. The main motivation for this study is to investigate the vulnerability of seismic code designed multistory steel moment resisting frame buildings to progressive collapse, and to propose retrofit solutions for those buildings that show to be prone to progressive collapse. \nThe studied buildings had 5, 10, and 15 stories (representing low-rise, medium-rise, and high-rise buildings), where each building was designed for three seismic zones (representing low, medium, and high seismicity). All studied buildings have a 3-bays x 6-bays rectangular plan; each bay has a span of 6 meters. Alternate Path Method (APM) recommended by GSA 2003 guidelines is adopted to evaluate the robustness of the buildings against progressive collapse. Three-dimensional models of the buildings are built using the Extreme Loading for Structures (ELS) software, where nonlinear static and nonlinear dynamic time history analysis are conducted for six different column removal scenarios for each building. \nThe nonlinear dynamic analyses showed that buildings designed for low seismicity do not possess sufficient resistance against column removal cases, thus need to be retrofitted to safeguard against the possibility of their progressive collapse. Consequently, two retrofit methods using top beams grid system and top gravity truss system are proposed for buildings in low seismic zones in order to enhance their robustness against progressive collapse. The nonlinear static and nonlinear dynamic analyses of the retrofitted buildings using the ELS software showed the effectiveness of the proposed retrofit systems in mitigating progressive collapse.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".