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Adaptability of triangular TL tower design with Schifflerized angle sections

2020· article· en· W4235068483 on OpenAlexaboutno aff
B. Ramesh, K. Balaji, B. Santhosh Kumar

Bibliographic record

VenueInternational Journal of Emerging Trends in Engineering Research · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTowerAdaptabilityEngineeringMaterials scienceStructural engineeringBiology

Abstract

fetched live from OpenAlex

The triangular shape towers have been widly used in transmission line towers. Generally, the profile legs are designed with either angular sections or tubular sections. As the triangular tower vertices are inscribed at 60 internal angle, the connection between the bracings and the profile legs can be achieved by bending 15 inward either the gusset plate or the angle of the profile leg. The manufacturing process of hot rolled 60 o is not yet developed fully, the other alternative of schifflerized angle sections have been adopted in the design of the 60m height Transmission Line tower design. The IS 802 code procedure stipulates the design requirements of TL Towers.However, no special provision for schifflerized angle sections has been made in this code. The Canadian Standardcode-(CSA S16-09) has specified the distinctive provisions for the design of schifflerized sections. The relevant buckling strength equations of Canadian standard and IS 802-2016 are approximately the same. Hence strength and economic viability of schifflerized angle sections with conventional 90 o angle section of the 60-meter tower with broken wire conditions for a span 250 meter have been explored with E-tabs software that the 10% weight savings have been achieved with the schifflerized angle sections for the same strength of the conventional angle sections.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.064
GPT teacher head0.334
Teacher spread0.270 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

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