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Record W1938241006

Failure Probabilities of Existing Overhead Shield Wires

2008· article· en· W1938241006 on OpenAlexaff
Ibrahim Hathout, Le Anh Uyen Vu

Bibliographic record

VenueProceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsUniversity of WaterlooHydro One (Canada)
Fundersnot available
KeywordsGumbel distributionShieldStormCumulative distribution functionStructural engineeringRange (aeronautics)GeologyEnvironmental scienceGeotechnical engineeringEngineeringExtreme value theoryProbability density functionMathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

The objective of this paper is to introduce analytical models for accurate prediction of failure probabilities of existing shield wires and to recommend a time window for wire replacement at an acceptable failure threshold. Ductile and strength tests results and failures records of shield wires were examined to determine the trend of wires deteriorations as they age. Since most of failures occurred due to ice accretion during winter storms and wire deteriorations due to corrosion; an ice accretion model and corrosion models were used to predicts wire tensions and wire deteriorations during service life. To calculate the probability of failures, the strength and cross section area were assumed to be random variables modeled using normal distributions. The wire tensions due to ice accretions were calculated from the last 50 years of recorded winter storms. Anderson-Darling goodness of fit test was used to determine the best statistical model to fit the maximum annual wire tensions. The Gumbel distribution passes the test and was found to give stable results over wide range of tested conditions. Limit state function was developed and a computer program was written to determine the failure probabilities of shield wires using Rackwitz-Fiessler algorithm.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.098
GPT teacher head0.322
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations1
Published2008
Admission routes1
Has abstractyes

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