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Record W2589013735 · doi:10.5006/c2013-02858

Application of Plastic Strain Damage Models to Characterize Dent with Crack

2013· article· en· W2589013735 on OpenAlexaffabout
Udayasankar Arumugam, Ming Gao, Ravi Krishnamurthy, Rick Wang, Richard Kania, David C. Katz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsMaterials scienceStrain (injury)Composite materialForensic engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Currently, the allowable strain limit for a plain dent is 6 percent as per ASME* B31.81 for gas pipelines. Field experience has shown that the 6% strain limit for plain dents could be overly conservative, which can result in unnecessary excavations and repairs. Recently, efforts to develop an alternative strain limit have been proposed. In this paper, two plastic damage-based models, namely, ductile failure damage and strain limit damage, and one minimum elongation-based criterion are reviewed. Attempts have been made to use these models to characterize rock dents associated with cracks in terms of a plastic damage severity factor and its susceptibility to crack initiation. Field excavations and finite element analysis are utilized to validate these models using two real pipeline dents from two different pipeline operators, operating in USA and Canada. The results have shown that the internal cracks were formed at the time of the initial indentation and can be predicted by the plastic strain damage based ductile failure models. On the basis of this, a newly developed approach that combines in-line inspection (ILI) technologies (caliper and magnetic flux leakage [MFL]) is introduced and utilized to discriminate between dent with corrosion and dent with crack, and identify critical dents in the 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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.169
Teacher spread0.160 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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