MétaCan
Menu
Back to cohort
Record W2135484375 · doi:10.1115/omae2013-10952

Employing Visual Image Correlation for the Measurement of Compressive Strains for Arctic Onshore Pipelines

2013· article· en· W2135484375 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStrain gaugeMaterials scienceCompressive strengthUltimate tensile strengthBendingStructural engineeringCompression (physics)BuckleTension (geology)Composite materialGeotechnical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

The Arctic onshore environment contains regions of discontinuous permafrost, where pipes may be subject to displacement-controlled bending in addition to high hoop stresses due to the pressurized fluids being transported. Considering the displacement-controlled nature of the deformations, strain-based design methodologies have been developed for permafrost pipelines when they are subject to bending and tension, which limit the longitudinal compressive and tensile strains. The widely accepted methodology in the industry to obtain the compressive strain capacity of line pipes subject to bending is to conduct Finite Element Analysis, incorporating material and geometrical nonlinearity calibrated against benchmark full-scale tests (bend tests) [1,2]. During these tests, compressive strains can be measured by various methods. The seemingly obvious choice is to apply strain gauges along the compression face of the specimen with respect to bending (intrados). This method will provide reasonable results until the compressive strain pattern begins to vary due to the initiation of buckle formation, which typically occurs shortly after yield. In order to measure average compressive strain beyond yield and up to buckling, the method used by C-FER Technologies (C-FER) involves using rotation measurement devices (inclinometers) to calculate the strain change between the most compressive and tensile fibres of the specimen (intrados and extrados, respectively) with respect to the bending direction. This value is then subtracted from the tensile strain gauge readings as measured by the strain gauge(s) located on the extrados of the specimen. The average compressive strain values derived from the inclinometer and extrados strain gauge measurements are based on the assumption that the plane sections remain plane. Recently, five large diameter pipes were bend-tested at C-FER’s testing facility in Edmonton, Alberta. In addition to the compressive strain measurement method used by C-FER described above (C-FER method), a visual image correlation (VIC) camera system was used to survey the strain distribution on the compressive face of the specimens. This paper gives a brief description of the test setup and instrumentation of this test program. The VIC camera setup and measurement technique are described and the overall strain distribution on the bending intrados as measured by the VIC cameras is presented. Strain measured by the VIC system is compared with gauge measurements at local points as well as the average compressive strain behaviour of the specimens obtained through the C-FER method described above. The results show that the VIC system can be a candidate to replace the conventional measurement techniques employed for compressive strain limit testing in support of strain-based design of arctic 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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.235

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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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