Section Force Correlation under Dynamic Wind Excitation of Balanced Cantilever Bridges
Bibliographic record
Abstract
The design of the lower pylon and the foundation of large balanced cantilever bridges is often dominated by dynamic response to turbulent wind, and capacity verification relies heavily on cross-section analysis considering moment-force relations.This paper proposes a consistent method for general quantification of section force correlation effects, based on industry-standard response calculations in the frequency domain.The method implies that all possible combinations of any two section forces can be determined and considered in design verifications.Thus, the method can replace a simpler, and sometimes otherwise required approach where individually maximized components are assumed to act in full correlation.The method is verified using advanced time-domain wind response simulations, which allow for direct assessment of section force correlations.The adopted time-domain wind response simulations are fully consistent with the frequency-domain calculations and include accurate representation of turbulence coherence and motion-induced forces.Correlation regimes determined for displacements and different section force components using time-and frequency-domain calculations match closely, thus validating the proposed method.It is demonstrated for a generic 2x130m balanced cantilever that the presented method can lead to more cost-effective and sustainable solutions, e.g.via eccentric arrangement of internal prestressing in pylon legs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".