Robustness-based assessment and monitoring of steel truss railway bridges to prevent progressive collapse
Bibliographic record
Abstract
Risks of bridge collapse were and continue to be real as evidenced by classical (e.g. Québec Bridge, Canada 1919; Seongsu Bridge, South Korea 1994) and recent (e.g. Skagit River Bridge, USA 2013; Francis Scott Key Bridge, USA 2024) episodes of catastrophic collapses. The causes of each collapse are diverse (e.g. natural disasters, changing conditions, design errors, intentional attacks). Still, the conclusions are always the same: deaths, injuries and large amounts of direct and indirect economic losses. In order to avoid these catastrophes, structural robustness and monitoring strategies can be used to analyse the bridge's vulnerability and anticipate any local-initial failure that can spread to the whole structure in the form of a progressive collapse. The objective of this work was to use an integrative threat-dependent and threat-independent approach to analyse the structural robustness of a never-before-studied U-shaped open cross-section steel truss railway bridge structure. Eight failure scenarios were considered and analysed through computational modelling. The extracted results make it possible: (i) to connect structural robustness analysis outputs with the definition of a new structural health monitoring strategy of the bridge; and (ii) to implement the conclusions in the real bridge with more than 100 sensors and a non-assisted alarm system for preventing progressive collapse. • Structural robustness analysis defines a new strategy for the structural health monitoring of steel truss bridges. • An integrative threat-dependent and threat-independent approach to analyse the structural robustness is used. • U-shaped open cross-section steel truss railway bridge structure is studied for the first time. • U-shaped cross-section bridge has more potential to experience problems than bridges with closed-box cross-section. • Implementation in a bridge with more than 100 sensors and a non-assisted alarm system for preventing 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".