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Record W4389607681 · doi:10.1139/cgj-2023-0419

Effect of water pressure on permeability of foam-conditioned sands for EPB shield tunneling

2023· article· en· W4389607681 on OpenAlexvenueno aff
Zhiyao Feng, Shuying Wang, Tongming Qu, Xiangcou Zheng

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPermeameterGeotechnical engineeringPermeability (electromagnetism)Pore water pressureWater pressureGeologyHydraulic conductivityShieldSoil scienceChemistrySoil waterPetrology

Abstract

fetched live from OpenAlex

Foam conditioning is a widely adopted technique in earth pressure balance (EPB) shield tunneling for the purpose of reducing sand permeability and preventing water spewing. The permeability of foam-conditioned sands differs from that of natural sands due to the presence of foam bubbles. This study investigated the effect of water pressure on the permeability of foam-conditioned sands using novel laboratory permeability tests. The water pressure, for the first time, is decoupled with the hydraulic gradient, owing to a newly developed permeameter with the controllable downstream hydraulic pressure in the laboratory. The results show that the permeability is significantly affected by the water pressure, and the effect is also predominantly dependent upon the foam injection ratio. The initial hydraulic conductivity increases with the increasing water pressure, while the initial stable period duration decreases. The water-plugging structure formed by foam bubbles and sand particles is prone to be damaged under high water pressure due to the shrinkage of foam bubbles. This means that the existing permeability tests with low water pressure underestimate the permeability of foam-conditioned sands. The underlying mechanism of water pressure in modifying the permeability of foam-conditioned sands is also examined from a particle-scale perspective.

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 score0.309
Threshold uncertainty score0.395

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 head0.008
GPT teacher head0.223
Teacher spread0.214 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

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