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Record W2900297876 · doi:10.1115/ipc2018-78466

Operational Experiences With Axial Strain Inline Inspection Tools

2018· article· en· W2900297876 on OpenAlexaff
Douglas Dewar, Greg Van Boven, Mohamed ElSeify, Paul Bjorn, N. Bruce

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsPositive Living North
Fundersnot available
KeywordsMagnetostrictionStructural engineeringMagnetic flux leakageMaterials scienceBendingComputer scienceMechanical engineeringEngineeringMagnetic fieldMagnetPhysics

Abstract

fetched live from OpenAlex

Axial Strain Inline Inspection has transitioned from an experimental to commercial technology that will develop significantly as the industry requires. Axial strain tool measures total elastic longitudinal strain on a pipeline including: imposed strains due to manufacturing; construction/cold bending; backfilling; and loading associated with abnormal forces such as ground movement and settlement. The technology is based on magnetostriction, which measures the permeability and magnetic induction of ferromagnetic materials. Magnetostriction is well understood, but the application of the technology to active pipelines is relatively recent. Currently, Inertial Measurement Unit (IMU) inline inspections (ILI) effectively identify areas of localized bending strains and can be used for monitoring of pipeline movements run to run, but they do not detect axial strain associated with either tensile or compressive loading. Currently, axial strain modules are mounted behind Magnetic Flux Leakage (MFL) platforms and have either 4 or 8 probes that provide circumferential readings typically at 0.5 to 1 m intervals. Data is either considered “trend” or “calibrated” depending on whether representative test samples are available. Interpretations are provided by the vendor in the form of Axial Strain Variation which is the averaged value of a set of readings with the hoop strain component removed. Additionally, data from each probe is analyzed to establish the maximum and minimal longitudinal strains (εmax/εmin) with locations around the circumference of the pipeline. Given the potential complexity of locked-in strains, simple calculations using sinusoidal bending relationships do not apply. Therefore, curve fitting analysis is required to determine the circumferential strains. This paper includes operational learnings from the analyses of data from eight (8) Axial Strain ILI runs within variable terrain on some natural gas transmission and gathering pipelines in British Columbia by verifying strains due to known abnormal loading as well as identifying previously unknown features (landslides, in particular). In addition, sources of error, data anomalies, current limitations and potential improvements of the technology are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.263

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)0.0000.000

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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