Assessment of the Failure of an Electrical Transmission Line Due to a Downburst Event
Bibliographic record
Abstract
In September 1996, Manitoba Hydro reported damages of about $10 Million (US) due to failure of 19 transmission towers during a downburst event. As a result, a research program has been initiated in Canada to study the performance of transmissions tower under high intensity wind loads associated with localized events such as downbursts and tornadoes. This study focuses on assessing the failure of one of the towers that collapsed in Manitoba, Canada, during the 1996 downburst event. The study is conducted numerically using a computer code that was developed in-house at the University of Western Ontario specifically for the analysis of transmission towers under the effect of downbursts. The numerical model combines wind field data for downbursts generated using a Computational Fluid Dynamic (CFD) simulation together with non-linear finite element formulation that accounts for the large displacement behaviour of the conductors. Failure criteria for the tower members associated with both compressive and tension forces are incorporated into the model. As such, the non-linear finite element model is capable of predicting the progressive failure of a tower that might initiate once one of its members reaches its ultimate capacity. A parametric study is conducted to determine the downburst. parameters, such as its radius and location relative to the tower, that are most critical for the structure in terms of failure. Using these parameters, the velocity of the downburst is gradually increased till one of the tower members reaches its ultimate capacity. Failures of other members progressively occur till the structure looses its overall stability. The study provides an insight about the mode of failure of transmission tower structures under downburst loading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".