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Record W2315713888 · doi:10.1061/40642(253)32

Reliability-Based Design of Transmission Line Structures - Direct Approach Using the Inverse Reliability Method

2002· article· en· W2315713888 on OpenAlexaff
Hong Li, Gouri Bhuyan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsServiceability (structure)Structural engineeringTransmission lineReliability (semiconductor)InverseReliability engineeringDesign methodsDeflection (physics)Computer scienceEngineeringMathematicsMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

A reliability-based design procedure using the inverse reliability method is proposed in this paper for design of overhead transmission structures. The procedure allows direct and fast determination of design parameters such as wind span and pole groundline circumference, in order to meet pre-specified target reliabilities. Two design applications are illustrated: 1) calculation of the design wind span of a lattice steel suspension tower so that a target reliability is achieved in a compression performance criterion; and 2) determination of design wind span of an H-frame tangent wood pole structure to achieve target reliabilities associated with design criteria in tip deflection (serviceability criterion) and bending (ultimate design criterion). The advantages and implications of these approaches are discussed.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.195
GPT teacher head0.354
Teacher spread0.158 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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