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Record W3210527794 · doi:10.1139/cgj-2021-0196

Effect of bentonite slurry on the function of foam for changing the permeability characteristics of sand under high hydraulic gradients

2021· article· en· W3210527794 on OpenAlexvenueno aff
Fanlin Ling, Shuying Wang, Qinxin Hu, Shuo Huang, Zhiyao Feng

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteSlurryPermeability (electromagnetism)Geotechnical engineeringMuckHydraulic conductivityConditioningMaterials scienceGeologyComposite materialSoil scienceChemistrySoil water

Abstract

fetched live from OpenAlex

During earth pressure balance (EPB) shield tunnelling in sandy ground, not only foam but also other conditioning agents need to be injected to reduce the permeability of muck and avoid water spewing out of the screw conveyor. Permeability tests were carried out to study the permeability characteristics of conditioned sand under high hydraulic gradients. A low bentonite slurry injection ratio (BIR) enhanced the workability of foam-conditioned sand. As the hydraulic gradient increased, the initial permeability coefficient of conditioned sand increased, and the initial stable period became shorter or disappeared. The BIR had a more significant effect on the permeability of conditioned sand than the foam injection ratio (FIR), and this effect gradually weakened as the hydraulic gradient increased. The initial permeability coefficient of the foam–bentonite slurry-conditioned sand decreased by approximately an order of magnitude compared with that of the foam-conditioned sand. With the addition of bentonite slurry, suitable sand conditioning can accept a higher water content (w) and lower FIR, resulting in suitable ranges of w and FIR that are more flexible. Finally, the mechanism of stabilizing foam under the action of bentonite slurry was discussed by considering the interaction between foam bubbles and fine particles.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.323

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations34
Published2021
Admission routes1
Has abstractyes

Explore more

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