Dynamic behaviour of pre-stressed concrete transmission poles under synoptic wind loading
Bibliographic record
Abstract
A numerical model capable of predicting the dynamic response of pre-stressed concrete transmission poles under both the mean and fluctuating components of synoptic wind loads is developed in this study. A full non-linear dynamic analysis is conducted under a time history variation of wind velocity. The peak total responses, such as conductors’ reactions and poles’ base moments, are determined from this analysis. The same analysis is repeated in a quasi-static manner. Dynamic amplification factors (DAF), defined as the ratio between the maximum response based on a non-linear dynamic analysis and the corresponding value based on a quasi-static analysis, are calculated for the poles and the conductors to quantify the dynamic impact of synoptic wind loads. This factor is used to assess the importance of including the resonant component while estimating the response of the transmission poles. In addition, gust response factors (GRF) defined as the ratio between the peak and mean responses are evaluated and compared to GRF recommended by ASCE-74 (2010). A parametric study is conducted on three pre-stressed concrete transmission line systems. The mean value of the incoming wind speed is the main variable included in the parametric study. It is found that the resonant effect is dominant in the conductors at low wind speeds and the poles exhibit high dynamic response at higher wind speeds.
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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 | 0.001 | 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".