Buffeting-Induced Fatigue Damage Assessment of a Long-Span Bridge under a Changing Climate Scenario
Bibliographic record
Abstract
With the continuous increase of bridge spans, wind-induced vibrations will pose serious problems to structural integrity and serviceability. Among the many vibration sources of long-span bridges, buffeting, which results from impinging turbulence, affects the fatigue life of the bridge structure, and when coupled with other wind-induced loads, might lead to severe structural problems. With climate change, buffeting-induced fatigue might significantly increase due to higher wind speeds and turbulence intensities. Therefore, it is important to assess the cumulative fatigue damage generated by buffeting loads under changing climate scenarios. In this study, the buffeting response of a single-span suspension bridge is investigated in the frequency domain under the Worst-case climate scenario RCP8.5. A simplified bridge model will be used to capture key dynamic and aerodynamic characteristics, with a time-dependent Weibull distribution accounting for nonstationarity in wind speed. The analysis of fatigue damage accumulation under buffeting will involve the rain-flow cycle counting method and the Palmgren–Miner damage law. Three Canadian cities, Montreal, Toronto, and Vancouver, will be included in the study to assess how location, along with the climate scenario, influences the life-cycle buffeting-induced accumulated damage over a 100-year period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".