A novel robustness index for progressive collapse analysis of structures considering the full risk spectrum of damage evolution
Bibliographic record
Abstract
The search for a bona fide robustness index to quantify the system performance upon unexpected disturbances has been a continuous research effort. Previous studies have largely neglected to reflect the damage evolution feature of a progressive collapse failure. To bridge this important knowledge gap, this paper presents a novel risk-based robustness index for structures prone to disproportionate progressive collapse. Defined on a new concept, referred to as damage evolution curve, the proposed index considers the full spectrum of risk due to initiating events, and rigorously quantifies the impact of intermediate partial damages on the robustness assessment. Examples using a Daniels system, a truss, and an idealised moment-resistant frame are presented to illustrate how the proposed index is applied for assessing the robustness of structures with increasing complexity. In addition, a comprehensive comparative study against four existing robustness indices is carried out. It is shown that the proposed index provides consistent results for different influencing factors, whereas the other indices either respond counter-intuitively or are insensitive to some of the factors. With consideration of intermediate damage and failure consequences, the development of the proposed robustness index represents an important step towards the performance-based design 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".