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Record W2372535576

Research on the Composite Foundation Anti-deformation Performance and Optimal Thickness of Electricity Transmission Towers in Mining Subsidence Area

2012· article· en· W2372535576 on OpenAlexaff
Yunfei Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeomechanics and Mining Engineering
Canadian institutionsImpact
Fundersnot available
KeywordsFoundation (evidence)Deformation (meteorology)TowerTransmission towerSubsidenceComposite numberStructural engineeringGeotechnical engineeringFinite element methodEngineeringTransmission (telecommunications)GeologyMaterials scienceComposite materialElectrical engineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

In this paper,the finite element models are established for typical kinds of electricity transmission tower.The anti-deformation performances of single foundation and composite foundation with different thickness protection plate are studied with numerical simulation.The study indicates that,compared to the single foundation,the horizontal displacements of supports and the stresses of the upper structure are distinctly decreased by the composite foundation.The study also indicates that,the improvement of anti-deformation performance by increasing thickness of protection plate played an important role only in a limited range,the feasible minimum thickness of the plate is about 1/45-1/35 of the distance between two supports.As a result,the study lends a support to the improvement of the foundation design method for the transmission tower in the subsidence area.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.036
GPT teacher head0.262
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2012
Admission routes1
Has abstractyes

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