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Acoustic Emission Sensing of Pipe–Soil Interaction: Full-Scale Pipelines Subjected to Differential Ground Movements

2019· article· en· W2971137478 on OpenAlexaffabout
Alister Smith, Ian D. Moore, Neil Dixon

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsAcoustic emissionGeotechnical engineeringDeformation (meteorology)Pipeline transportStrain gaugeFull scaleSoil structure interactionGeologyMaterials scienceStructural engineeringEnvironmental scienceEngineeringFinite element methodComposite material

Abstract

fetched live from OpenAlex

This paper presents the first full-scale demonstration of the potential use of pipe–soil interaction-generated acoustic emission (AE) for early detection of buried pipe deformation. Full-scale tests were performed at the buried infrastructure research facility at Queen’s University, Canada, using a split box apparatus to impose differential ground motion on a steel pipe buried in dry sand, and to investigate the influence of stress level and patterns of deformation on AE generation. The pipe was instrumented with AE sensors, strain gauges, fiber optic strain sensing, and linear potentiometers, and surface deformation was measured using an automatic total station. AE measurements were used to interpret the evolution of the pipe–soil interaction behavior. AE activity correlated strongly (R2 from 0.83 to 0.99) with both the rate and magnitude of pipe deformation at different burial depths, and quantified relationships are presented that enable interpretation of pipe–soil interaction behavior from AE measurements.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.178
Teacher spread0.175 · 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.

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

Citations29
Published2019
Admission routes2
Has abstractyes

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