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Record W1990574222 · doi:10.1115/ipc2006-10045

Development of Reliability-Based Design and Assessment Standards for Onshore Natural Gas Transmission Pipelines

2006· article· en· W1990574222 on OpenAlexaffabout
Joe Zhou, Brian Rothwell, Maher Nessim, Wenxing Zhou

Bibliographic record

VenueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations · 2006
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsPipeline transportPipeline (software)Reliability (semiconductor)EngineeringNatural gasRisk analysis (engineering)Natural gas industrySafety standardsConstruction engineeringSystems engineeringManufacturing engineeringReliability engineeringMechanical engineeringBusinessWaste management

Abstract

fetched live from OpenAlex

Onshore pipelines have traditionally been designed with a deterministic stress based methodology. The changing operating environment has however imposed many challenges to the pipeline industry, including heightened public awareness of risk, more challenging natural hazards and increased economic competitiveness. To meet the societal expectation of pipeline safety and enhance the competitiveness of the pipeline industry, significant efforts have been spent for the development of reliability-based design and assessment (RBDA) methodology. This paper will briefly review the technology development in the RBDA area and the focus will be on the progresses in the past years in standard development within the American Society of Mechanical Engineers (ASME) and the Canadian Standard Association (CSA) organizations.

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)0.036
metaresearch head score (Gemma)0.064
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and RegulationsSame topicOffshore Engineering and TechnologiesFrench-language works237,207