Robustness Assessment of Reinforced Concrete Frames under Progressive Collapse Hazards: Novel Risk-Based Framework
Bibliographic record
Abstract
Robustness assessment is an important component for performance-based progressive collapse design. However, existing methods either do not consider the cascading failure feature of progress collapse, or fail to recognize the randomness in material, geometrical and loading parameters. This paper presents a novel robustness assessment methodology for progressive collapse design of reinforced concrete frames. The proposed methodology includes several novelties: First, it uses a new risk-based robustness index recently developed by the authors. The index quantifies the whole spectrum of risk caused by initiating hazardous events. Second, it includes a unique directional simulation technique, making probabilistic nonlinear pushdown analysis a computationally affordable task. Finally, the assessment can assist in determining if an enhancement design is warranted for such a low-probability-high-consequence event. The study examined four different frame designs to evaluate the effectiveness of seismic and progressive collapse design provisions. The Alternate Path Method (APM) was shown to improve structural robustness significantly, although achieved with considerable additional cost. Ductility designed for seismic loading was also shown to be beneficial for structural robustness against 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".