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Record W4220888393 · doi:10.1155/2022/4498458

A New Stress Monitoring Method for Mechanical State of Buried Steel Pipelines under Geological Hazards

2022· article· en· W4220888393 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.

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Alberta
FundersChinese Academy of Sciences
KeywordsFinite element methodStrain gaugePipeline transportStress (linguistics)Structural engineeringPipeline (software)Materials scienceInstallationRange (aeronautics)Approximation errorComputer scienceMechanical engineeringEngineeringComposite materialAlgorithm

Abstract

fetched live from OpenAlex

Long-distance pipelines are threatened by a variety of natural geological hazards. A stress monitoring system driven by the strain-stress solution algorithm was proposed; it can achieve real-time maximum axial stress measurement by installing vibrating wire gauges (VWGs) on the surface of the pipe. To verify the effectiveness of the algorithm, a large-scale pipe mechanical loading experiment combined with a finite element model (FEM) was conducted. The results show that VWGs were reliable with a relative error of 1.19%∼7.98% compared with resistance strain gauges (SGs). The FEM was also reliable with a maximum relative error of 4.04% compared with theoretical analysis. When the reasonable combination modes of VWGs were chosen utilizing the least square method, the error of the pipe stress detection algorithm could be controlled within the range of −13.33∼16.66%. This pipeline stress monitoring technology can meet the requirement of 24-hour dynamic monitoring of the underground pipeline’s mechanical state, realizing the early warning of geohazards.

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)0.001
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 score0.130
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.299
Teacher spread0.283 · 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