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Record W2335328939 · doi:10.1061/40642(253)6

1.0 The Need for Reliability-Based Design

2002· article· en· W2335328939 on OpenAlexaff
Richard Aichinger, Nelson G. Bingel, Gary E. Bowles, Habib J. Dagher, James W. Davidson, Fouad Fouad, Magdi Ishac, Brian Lacoursiere, Wesley J. Oliphant, Ronald E. Randle, Martin Rollins, Camille G. Rubeiz, Larry Vandergriend, Michael Voda, David West, Ron Wolfe, C. Jerry Wong, Alec Zolotoochin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsHydro One (Canada)BC Hydro (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringStructural reliabilityElectric power transmissionComplement (music)EngineeringComputer scienceFoundation (evidence)Distribution (mathematics)LawElectrical engineeringMathematicsPolitical science

Abstract

fetched live from OpenAlex

There is a need to provide a design methodology for distribution and transmission poles that yields consistent reliabilities across all material types: wood, steel, concrete, fiberglass and other new materials. Prevailing US practice and most state laws require that transmission and distribution lines be designed, with a minimum, to meet requirements of recent editions of the National Electric Safety Code. (NESC, 2000) The NESC rules for the selection of design load and strength factors are largely based on successful experience, but they do not have a strong theoretical foundation. Some designers find the rules too restrictive, while others adopt more conservative criteria. In fact, individual utilities often develop their own loading agendas to complement the NESC rules. A desire to achieve more consistent reliabilities across materials was the impetus for the development of ASCE Manual 74 Guidelines for Transmission Line Structural Loading and the IEC. While Manual 74 offers consistent methods to calculate loads, there is still a need to provide consistent methods to calculate strength across pole materials.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.293

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 head0.037
GPT teacher head0.209
Teacher spread0.173 · 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
GenreMethods

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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