Assessment of End Tower Response to Downburst Wind Loads: Experimental and Numerical Studies
Bibliographic record
Abstract
Natural hazards pose a significant threat to community resilience by disrupting power distribution systems, leading to widespread and frequent power outages. End towers are critical structures in a transmission line system that should contain the cascade failure of the towers from progressing along the line. Previous research has extensively investigated the behavior of tangent towers, which are the typical supporting towers along the line, under extreme wind loads. To the best of the authors’ knowledge, this study is the first to assess the effect of downburst loads on end towers numerically and experimentally. An aeroelastic test is carried out at the Wind Engineering, Energy and Environment (WindEEE) Research Institute on a 1:65 model of a transmission line that includes an end tower and two tangent towers. The transmission line is subjected to two simulated downbursts having different jet velocities, while considering different locations of the downburst relative to the end tower. The critical downburst configurations that cause the maximum transverse and longitudinal base shear force for the end tower are identified. Furthermore, the experimental data are used to investigate the dynamic amplification factor of the end tower under downburst loads. Finite element analysis of the tested line is conducted under the simulated downburst loads to assess the adequacy of the ASCE-74 provisions in calculating the downburst loads for end towers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".