Equivalent static wind load procedure for skew winds on large bridges
Bibliographic record
Abstract
This paper describes theoretical framework on forming equivalent static wind loads (ESWL) for large bridges. A method is proposed for efficient handling of large number of load cases, when vibration and structural analysis is extended to skew winds, i.e., to the wind directions other than the principal ones. These appear to be increasingly important in many practical cases when complex bridge geometry is used for architectural uniqueness; or when the bridge is situated in city centres or hilly terrain, where local obstacles make the wind turbulence difficult to assess with standard models. The method uses a set of load cases for principal wind directions to be input and solved with the static Finite Element (FE) model. Combination matrix is deduced for the results to assess skew winds. The method is alike that is frequently used in wind-tunnel studies of tall buildings. ESWL determination is done in co-operation with the wind and the bridge engineer. The needed input for the wind engineer includes numeric vibration mode shape data, global nodal coordinates and mass distributions. ESWL are created in numeric form that could be easily input to the FE-model. The method allows utilisation of various type analysis results and experimental data available for the bridge, including section-model based analysis, full-model wind-tunnel tests and structural monitoring results. It facilitates examination and adjustment of appropriate safety marginal to wind loads that take into account methodologic uncertainties in each. It is proposed that wind-tunnel laboratories, or other wind engineers with bridge analysis expertise, should more often include ESWL-extraction to their services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".