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Record W1984890718 · doi:10.1115/ipc2006-10101

Calculation of Strains in Dents Based on High Resolution In-Line Caliper Survey

2006· article· en· W1984890718 on OpenAlexaff
Stanisław Łukasiewicz, Jarosław Czyż, Chao Sun, Samer Adeeb

Bibliographic record

VenueVolume 2: Integrity Management; Poster Session; Student Paper Competition · 2006
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsTransCanada (Canada)Calgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsCalipersFinite element methodCurvatureStructural engineeringBendingShell (structure)Computer scienceEngineeringEngineering drawingMechanical engineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

The strain level in a deformed pipe wall has been included in recent years in the assessment of severity of dents in order to better prioritize and effectively repair mechanical damage in pipelines. High resolution caliper data provide accurate dent shape that can be used for determination of strains using commercial Finite Element Method (FEM) programs. However, those programs, designed to solve a variety of general problems, are very inefficient in calculation of strains in a pipewall with known radial displacements. This paper presents a very efficient method that combines analytical techniques with FEM approach. After properly filtering the caliper measurements the bending strain is calculated from the pipewall curvature, and the membrane strain is obtained using a FEM shell model that includes nodes with only two degrees of freedom in the mid-plane of the wall.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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 designObservational
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

Citations19
Published2006
Admission routes1
Has abstractyes

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Same venueVolume 2: Integrity Management; Poster Session; Student Paper CompetitionSame topicNon-Destructive Testing TechniquesFrench-language works237,207