New Method for Climate Change Resilience Rating of Highway Bridges
Bibliographic record
Abstract
While the authors of current bridge management systems (BMSs) have noted the need for expanding the systems to include more functional aspects of bridge performance, to date no method has been proposed for the incorporation of bridge rating against climate change impacts. This paper presents a new procedure for extending the asset management scope for highway bridges to incorporate the bridges’ resilience or vulnerability against climate change impacts. The proposed procedure draws from the projected demands of climate change—a worldwide phenomenon that is expected to produce its most dramatic impacts in cold regions. The following bridge resilience indicators are proposed: abutment washout, pier scour, abutment erosion, deck flooding, and abutment permafrost stability. The formulations for the proposed indicators require weights to be assigned to each indicator. The other requirement comprises capacity measures that indicate how well a bridge is equipped to withstand the projected climatic effects. The new procedure has been applied to 14 highway bridges in the Canadian Arctic to demonstrate how public investments in transportation infrastructure could be better managed and protected. The method comes with an Inspection Form that transportation agencies and their bridge inspectors can use for rating bridges on climate change resilience.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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".