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Record W2804228175 · doi:10.1115/1.4040314

Effects of Stress–Strain Characteristics on Local Buckling of X80 Pipe Subjected to Strike-Slip Fault Movement

2018· article· en· W2804228175 on OpenAlexaff
Xiaoben Liu, Hong Zhang, Onyekachi Ndubuaku, Mengying Xia, J. J. Roger Cheng, Yong Li, Samer Adeeb

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Alberta
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsBucklingStructural engineeringSlip (aerodynamics)Finite element methodGeotechnical engineeringStress (linguistics)Nonlinear systemStress–strain curvePipeline transportMaterials scienceGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The structural integrity of underground pipelines are subject to a major threat from permanent ground displacements when they cross active tectonic (e.g., strike-slip) faults, because of large strains potentially induced in pipes, leading to pipe buckling and possible rupture. In this paper, the buckling behavior of X80 pipe is studied numerically with an emphasis on the effects of steel stress–strain characteristics. A rigorous mechanics-based nonlinear finite element (FE) model of a buried X80 pipe crossing a strike-slip fault is developed using shell elements and nonlinear springs for the pipe and soil resistance, respectively. The pipe steel material in the FE model is characterized by a novel and versatile stress–strain relationship, which was established to successfully capture both the round-house (RH) type and the yield-plateau (YP) type stress–strain behaviors. This allows investigating the significant effects of the stress–strain characteristics, as observed in this paper, on the buckling behavior of pressurized and nonpressurized pipes.

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 score0.172
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.202
Teacher spread0.198 · 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".

Quick stats

Citations16
Published2018
Admission routes1
Has abstractyes

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