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Record W3121998015 · doi:10.1115/ipc2020-9655

Pipeline Plain Dent Fatigue Assessment: Shedding Light on the API 579 Level 2 Fatigue Assessment Methodology

2020· article· en· W3121998015 on OpenAlexaff
Zeynab Shirband, Adrian Gosselin, Stuart Guest, Lee Falcon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPipeline transportStructural engineeringStructural integrityPipeline (software)Stress (linguistics)Failure assessmentDeformation (meteorology)EngineeringExcavationIntegrity managementFatigue testingForensic engineeringStress concentrationGeotechnical engineeringFracture mechanicsMechanical engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract For continued safe operation of pipelines, thousands of integrity digs are conducted every year to repair ILI detected defects. Integrity-driven pipeline excavations can be quite costly, present significant scheduling challenges with landowner consultation and seasonal access limitations, and an unmitigated defect may have required a pressure reduction or service outage, resulting in a loss of revenue from the asset. Dents are known to be one of the drivers for many integrity excavations, especially for liquid pipelines. A pipeline with a minimal mechanical deformation is not expected to fail immediately, however, severe pressure cycles combined with the geometric distortion can cause fatigue crack initiation and growth that can lead to failure. To account for the possibility of fatigue failure, recent changes to pipeline codes, such as CSA Z662, are requiring pipeline operators to repair any dent susceptible to fatigue failure unless an engineering assessment proves it is fit for service. A commonly used dent fatigue assessment methodology is outlined in API RP 579, also known as the EPRG-2000 model. The assessment methodology uses an S-N curve from DIN 2413 part 1 with a safety factor of 10, which has been derived from undamaged pressurized pipe sections experiencing pressure cycles with stress ratios of zero, and separate stress enhancement factors for dents and gouges which take into account the shape of dents and gouges. To account for the effect of mean stress, Gerber mean stress correction, which has been developed for pressure cycles with stress ratios of −1 (i.e., for fatigue bar specimens), is also applied on pressure cycles. According to the literature, API 579 Level 2 fatigue assessment methodology results in very conservative estimates of fatigue lives compared to experimental data. This paper will discuss the potential factors resulting in conservative assessments and propose refinements in the methodology. This will include the safety factor used for pipes with known operating pressure fluctuations and the mean stress correction model suitable for a pipeline with pressure cycles that have R ratios greater than zero. The acceptable number of cycles obtained using the proposed refinements were compared to experimental data and EPRG-1995 model’s predictions — the comparison revealed that the proposed methodology results in a more realistic safety margin for dented pipelines. The proposed methodology can be used as a part of engineering assessments in mechanical damage integrity management programs to improve the pipeline operator’s understanding of a dent’s remaining life and enable a more appropriate repair timeline.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.161
GPT teacher head0.359
Teacher spread0.198 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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