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Record W1973784459 · doi:10.1115/ipc2010-31557

Application of Reliability Based Design and Assessment to Seismic Evaluations

2010· article· en· W1973784459 on OpenAlexaff
Maher Nessim, Nader Yoosef‐Ghodsi, Doug Honegger, Joe Zhou, Shanshan Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Pipeline transportReliability engineeringPipeline (software)Seismic analysisStructural engineeringComputer scienceFinite element methodSoil liquefactionLiquefactionEngineeringGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The application of reliability based design and assessment (RBDA) as a basis for seismic evaluations of natural gas pipelines is explored through analysis of a number of representative pipeline examples. To accomplish this, a simplified approach was developed to generate a representative probability distribution of permanent ground deformations due to soil liquefaction. An idealized pipeline alignment through a liquefiable layer under a river was defined, and a number of cases representing NPS12 and NPS36 pipelines in classes 1, 2 and 3 were analyzed using a finite element model. The probability of exceeding the strain limits for pipe body and girth weld were calculated and compared to the reliability targets. The results were used to identify diameter and class combinations that can meet the reliability targets, and to make preliminary conclusions regarding the viability of using RBDA for seismic evaluations.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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