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Record W2060905410 · doi:10.1115/omae2007-29714

A Standardized Approach for Reliability-Based Design and Assessment of Pipelines

2007· article· en· W2060905410 on OpenAlexaffabout
Maher Nessim, Wenxing Zhou, Joe Zhou, Brian Rothwell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Pipeline transportReliability engineeringPipeline (software)EngineeringSubmarine pipelineProcess (computing)Computer sciencePopulationMechanical engineering

Abstract

fetched live from OpenAlex

A new Annex on reliability-based design and assessment of onshore natural gas pipelines has been adopted for inclusion in the 2007 edition of the Canadian Standard Association’s pipeline standard (CSA-Z662). The same Annex is also being reviewed for inclusion in the next edition of the ASME gas pipeline code (ASME B31.8). The Annex defines a set of specific reliability targets as a function of pipeline diameter, pressure and population density; prescribes the process of demonstrating compliance with these targets; specifies the key requirements of a valid reliability calculation; and gives guidance on specific analysis methods, limit state functions and input parameter distributions. It is one of the few full reliability-based standards in the industry and is the first to mandate specific reliability targets. This paper describes the reliability-based approach adopted, the risk-based methodology used in defining the reliability targets and the key elements of the codified approach. It discusses current applications of the methodology as well as existing and potential plans developing similar codes for liquid and offshore pipelines.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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 designTheoretical or conceptual
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
Published2007
Admission routes2
Has abstractyes

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