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Record W4220886817 · doi:10.1139/cgj-2021-0525

Microscopic mechanism analysis of calcareous sand in electrolysis desaturation using <sup>1</sup>H L-F NMR

2022· article· en· W4220886817 on OpenAlexaffvenue
Runze Chen, Yumin Chen, Hanlong Liu, Xiaoguang Cai, Kang Wu, Zhe Zhang

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsElectrolysisCalcareousSaturation (graph theory)LiquefactionChemistryGeotechnical engineeringMaterials scienceGeologyElectrode

Abstract

fetched live from OpenAlex

Electrolysis desaturation is a novel method to improve the liquefaction resistance of liquefiable foundations. The microscopic mechanism of the horizontal-layered electrolysis desaturation of calcareous sand specimens was revealed using a low-field nuclear magnetic resonance ( 1 H L-F NMR) apparatus. The expansion and recovery of sand pores was detected, and variation pattern of saturation distribution was explored, as well as the generation and migration of gas bubbles. The test results show that the electrolysis mainly discharged free water from the specimen, and the content of bound water or mechanical bound water kept basically unchanged. The rapid generation of the bubbles could lead to the expansion of the sand pores. Within 3 h after stopping electrolysis, the macropores in the calcareous sand essentially returned to the initial state, and the expansion of micropores and mesopores also remained basically stable. The construction method of prolonged low-current electrolysis or short-time multiple electrolysis could be applied for on-site desaturated foundation treatment. The shape and size of the bubbles produced by electrolysis were related to the calcareous sand pores. The migration of gas after stopping electrolysis was mainly in the vertically upward direction, resulting in the fusion of bubbles and the formation of larger bubbles.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207