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Record W3122015688 · doi:10.1115/ipc2020-9329

Life Expectancy of Decommissioned Pipelines Under External Corrosion: Probabilistic Modeling

2020· article· en· W3122015688 on OpenAlexaff
Chee K. Wong, Markus R. Dann, R.C.K. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPipeline transportCorrosionReliability (semiconductor)Probabilistic logicReliability engineeringMonte Carlo methodPipeline (software)Environmental scienceLife expectancyService lifeLine (geometry)Pitting corrosionComputer scienceEngineeringMaterials sciencePopulationEnvironmental engineeringMechanical engineeringMetallurgyPhysicsStatistics

Abstract

fetched live from OpenAlex

Abstract The typical service lives of operational energy pipelines are up to 50–70 years. The existing lines would subsequently be decommissioned and replaced with new lines. As one approach to dealing with a decommissioned line is by leaving the line untouched in the ground, there exists uncertainty in the structural integrity of decommissioned lines under external corrosion over time. This paper introduces reliability-based integrity assessment of decommissioned pipelines that remain in the ground. Two limit state functions are developed to quantify (i) formation of perforations under pitting corrosion, and (ii) thinning of pipe wall thickness under uniform corrosion and initiation of pipe yielding under soil and traffic loading. Corrosion occurrence and growth are modeled as stochastic processes to account for its spatio-temporal uncertainty. The reliability and safe remaining lifetime of an entire pipeline sections are determined using enhanced Monto-Carlo simulations. A sensitivity analysis is performed to identify the governing factors of the life expectancy. The proposed integrity assessment is illustrated on an example.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.238
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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