Review of bridge design practice for water loads, scour, and ice action: opportunities for climate resilience
Bibliographic record
Abstract
An appraisal of historical and recent damage to bridges in Canada and internationally underlines the risks posed by water loads, scour and ice effects. More than 50% of bridge failures in North America are attributed to floods and hydraulic factors, including scour, debris impacts and ice effects. River ice is a unique challenge for bridges in northern climates, with estimates of annual average damages in Canada due to ice jams exceeding $100 million. The rising costs and damages associated with extreme weather events highlight the vulnerabilities of Canadian infrastructure to climate change. Potential changes in exposure to ice and flood hazards (from riverine, pluvial, and coastal sources) pose a threat to the integrity and sustainability of transportation infrastructure, including bridges and overpasses. The Canadian Highway Bridge Design Code contains provisions for evaluating water loads, scour and ice action for new bridges, and refers to the Transportation Association of Canada’s (TAC) Guide to Bridge Hydraulics for additional guidance. However, there are no provisions for evaluating potential climate change impacts on these loads and actions. A review of the current state of bridge design practice for evaluating, mitigating and adapting to water loads, scour, ice action and related climate change impacts was conducted. This included an appraisal of bridge design codes, standards and guidelines in Canada and other countries. The review identified current gaps, emerging best practices, and possible steps towards improving design guidance and the resilience of Canadian bridges and overpasses to water loads, scour, ice action and climate change.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".