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Record W2335697648 · doi:10.1115/ipc2008-64526

Compressive Strain Limits of High-Strength Linepipes

2008· article· en· W2335697648 on OpenAlexaff
Nobuhisa Suzuki, Joe Zhou, Masao Toyoda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsMaterials scienceCompressive strengthCompression (physics)Strain hardening exponentHardening (computing)BendingStructural engineeringComposite materialMechanicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Regression formulas are developed in order to predict the critical compressive strain of X80 linepipes subjected to pure bending. The regression formulas were derived using an analytical solution and two conversion functions. The analytical solution was previously proposed to calculate the critical compressive strain of a pipe subjected to axial compression, where hardening properties are taken into account. The original analytical solution was rewritten to include a strain-hardening exponent. Two conversion functions are defined to connect two critical compressive strains of a pipe under different loading conditions. One is a pressure-effect conversion function which connects two critical compressive strains of a pipe without and with internal pressure, respectively. The other is a compression-bending conversion function which also connects two critical compressive strains of a pressurized pipe which is subjected to compression and bending. The regression formulas were obtained using the improved analytical solution and the two conversion functions. The regression formulas yield good agreement with FEA results especially for high-strain linepipes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.418

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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 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

Citations6
Published2008
Admission routes1
Has abstractyes

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