Robustness versus redundancy of existing structures: critical review and application
Bibliographic record
Abstract
Progressive collapse is defined as the propagation of failure from local damage that results in structural collapse. Throughout history there have been many tragic building and bridge collapses that have increased the amount of interest and research in the field of progressive collapse, albeit more so for buildings than bridges. Even though there is no universally accepted definition of robustness, structural robustness can be generally described as the ability of a system to absorb an initial damage and not collapse. Although often used interchangeably with robustness, redundancy is defined as the ability of a system to carry additional load after the first member has failed. The objectives of this paper are to 1) present a critical review of definitions for robustness versus redundancy from a structural engineering perspective, accompanied by a review of relevant robustness measures published in literature, and 2) quantify the improvement in structural robustness of a bridge after strategically upgrading elements to mitigate a brittle failure mode of the system using holistic structural robustness and structural redundancy indices. The upgrade is completed on an existing truss bridge subjected to corrosion damage. Nonlinear static finite element (FE) analyses of the bridge in intact and damaged states are first performed to assess the structural robustness of the damaged system with respect to the intact version. A strategic upgrade is then proposed for the bridge to increase its robustness and redundancy and illustrate the application of the indices when improving the safety of existing structures. Research is ongoing to optimize upgrade schemes to increase structural robustness and structural redundancy while minimizing the cost and carbon footprint associated with structural repairs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".