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Record W4311449806 · doi:10.1139/cgj-2022-0098

Degradation of soil arching caused by suffusion in gap-graded soils

2022· article· en· W4311449806 on OpenAlexvenueno aff
Zheng Xiao, Zhigang Cao, Yuanqiang Cai, Jie Han

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterInternal erosionGeotechnical engineeringCompactionDiscrete element methodGeologyErosionPorositySoil science

Abstract

fetched live from OpenAlex

To investigate evolution of soil arching during suffusion in gap-graded soils, a new suffusion apparatus was developed for trapdoor tests under a horizontal seepage flow. Glass beads with fine-grain contents ranging from 15% to 45% were adopted to represent coarse-grain controlled soils and fine-grain controlled soils. Coupled computational fluid dynamics (CFD) and discrete element method (DEM) models were developed to further investigate the evolution of soil arching from microscopic views. At the early stage of suffusion, soil arching degraded rapidly under seepage loads in both coarse-grain controlled and fine-grain controlled soils due to the compaction of soil skeleton and the clogging of voids, which increased in the number of fine particles connected to the stress-transfer matrix. At the later stage, soil arching was nearly maintained as the soil skeleton became stable in the coarse-grain controlled soils. In the fine-grain controlled soils, soil arching degraded continuously due to continuous loss of fine particles connected to the stress-transfer matrix. The decrease in the contact number of fine particles also made the soil skeleton more vulnerable to suffusion. The degradation of soil arching resulted in an increase of surface displacement and a decrease of soil arching height in fine-grain controlled soils.

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 score0.031
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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
Published2022
Admission routes1
Has abstractyes

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