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Record W4407762581 · doi:10.1061/jsendh.steng-14160

Assessment of End Tower Response to Downburst Wind Loads: Experimental and Numerical Studies

2025· article· en· W4407762581 on OpenAlexaff
Abdelrahman H. Ahmed, Ashraf El Damatty

Bibliographic record

VenueJournal of Structural Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTowerWind engineeringStructural engineeringGeologyEngineeringEnvironmental scienceMarine engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.281
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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