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Record W4399878433 · doi:10.1139/cgj-2023-0497

Effect of water pressure on time-dependent permeability characteristics of sand conditioned with foam and bentonite slurry

2024· article· en· W4399878433 on OpenAlexvenueno aff
Shuying Wang, Fanlin Ling, Qinxin Hu, Tongming Qu, Junlong Shang

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBentoniteGeotechnical engineeringPermeability (electromagnetism)SlurryWater pressurePore water pressureComposite materialMaterials scienceGeologyChemistry

Abstract

fetched live from OpenAlex

During earth pressure balance shield tunnelling in water-rich sandy ground, both foam and other conditioning agents, such as bentonite slurry, are injected to prevent water spewing. Permeability tests were conducted to investigate how water pressure affects the permeability of sand conditioned with foam and bentonite slurry. Experimental results demonstrate that increasing water pressure at the top and bottom of the specimen extends the initial stable period of the permeability coefficient, significantly slowing down its growth rate during the fast growth period. Soil grain migration was observed in specimens exposed to sufficiently high water pressure. During the slow growth period, the permeability coefficient decreased as water pressure increased, and this decrease rate correspondingly decreased. Under a consistent hydraulic gradient, increased water pressure led to enhanced stability of foam bubbles and extended the time-dependent curves for the permeability coefficient. Furthermore, the relationship between chamber pressure dissipation and foam stability was discussed during the standstill period of shield machines. To prevent water spewing, it is recommended to use the permeability coefficient of the muck at the outlet of the screw conveyor with the lowest water pressure as the evaluation index during permeability testing.

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 score0.525
Threshold uncertainty score0.387

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

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