Robustness-Based Optimal Progressive Collapse Design of Reinforced Concrete
Bibliographic record
Abstract
Progressive collapse of structures is a cascading failure phenomenon with a failure consequence that is disproportionate to the direct damage of an initiating event. The Ronan Point Building collapse in 1968 triggered the start of research on disproportionate progressive collapse in building structural engineering. Intense research was followed after the Alfred P. Murrah Federal Building bombing in 1995 and the disastrous collapse of the World Trade Centre after aircraft strikes. Progressive collapse has essentially two distinct features: system- rather than component-level responses, and low probability and high consequences. The majority of existing design codes and standards consider progressive collapse implicitly, by improving the performance of a structure through the specification of minimum levels of strength, continuity, and ductility. On the other hand, existing explicit design procedures largely consist of a component-based design approach. This study proposes an innovative system-based and risk-informed decision making framework for progressive collapse design of reinforced concrete frames. Considering the full spectrum of risk due to initiating events, a novel risk-based robustness index is proposed as a system-level performance criterion for design. From a conventionally designed structure, an optimization pro- cess identifies optimal allocation of resources that results in a robust system. An efficient design frontier is defined based on optimal designs when varying the additional expenses provided for the improvement of robustness of a structure. The efficient frontier is then used to verify the cost effectiveness of the design alternatives in conjunction with the cumulative prospect theory. In order to assist in the decision-making process of progressive collapse design provisions, a risk-cost trade-off framework is proposed. The design framework establishes whether additional resources must be used to enhance the robustness of a structure, and when required, it further identifies the optimal expenses that should be used to prevent potential 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".