Prioritizing Bridge Rehabilitation Plans through Systemic Risk-Guided Classifications
Bibliographic record
Abstract
Although highway bridges are among the most critical components in transportation infrastructure systems, different jurisdictions typically allocate only limited annual budgets to address issues pertaining to aging and deteriorating bridges. In addition, most published studies have focused only on component-by-component risk analysis (i.e., risk of individual bridge failure/closure/becoming a safety hazard) to inform bridge rehabilitation project managers. However, the network-level cascade (systemic) impacts of an individual bridge(s) closure on the network-level are often dealt with through, for example, detouring, rather than a systemic-risk-guided strategy. In this respect, the current study first utilizes a complex network theoretic approach to quantify the topological characteristics of bridge networks and subsequently their network-level robustness and node vulnerability. These measures are then integrated into a multiscale (i.e., component and network) bridge classification platform guided by the systemic-risk consequences of possible bridge closure on the entire network. To demonstrate its application, the platform is operationalized in Canada on the Province of Ontario’s bridge network. It is found that component- and network-level measures are not correlated, which highlights the importance of considering the network-level measure to inform bridge rehabilitation decision making. The current study calls for a paradigm shift in the strategy guiding prioritizing bridge rehabilitation projects to account for the risk imparted by specific bridge criticality on the entire network, rather than solely on the individual bridge’s structural conditions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".