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Record W2976084340 · doi:10.1139/cgj-2019-0076

On evolving size and shape of gas bubble in marine clay under multi-stage loadings: microcomputed tomography (μCT) characterization and cavity contraction analysis

2019· article· en· W2976084340 on OpenAlexvenueno aff
Yi Hong, Junhui Zhang, L.Z. Wang, T. Liu

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleSiltAnisotropyMaterials scienceGeotechnical engineeringGeologyMechanicsStiffnessStress (linguistics)Composite materialPhysicsOptics

Abstract

fetched live from OpenAlex

Discrete bio-gas bubbles commonly form in fine-grained marine sediments and have modified many aspects of the behavior of these sediments, including strength, stiffness, and permeability. Although the level of such modifications is known to govern by bubble shape and size, limited studies have been undertaken, mainly due to difficulty in nondestructively characterizing bubbles within a soil under in situ stresses. In this study, a mini-loading device was developed to perform one-dimensional loading tests on gassy marine clay and gassy silt in a microcomputed tomography (μCT). The evolving bubble shape, size, and pressure during loading were quantified, and the resulting stress fields around the bubble cavities were evaluated via elliptical cavity contraction analysis considering stress anisotropy. As the vertical load increased, bubble cavities were found to compress predominantly along the vertical loading direction, with little horizontal compression, because localized soil failure (LSF) and thus cavity collapse occurred mainly near the roof of the at-rest lateral earth pressure coefficient (K 0 )-stressed elliptical bubble cavities. The evolution of bubble shape and size under loading is significantly affected by stress anisotropy, which governs the extent and location of the LSF. A Gaussian mixture model is adopted to quantify the evolving distributions of bubble structure parameters, which are essential for developing more physically rigorous gassy soil models.

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 score0.236
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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