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Record W2096449471 · doi:10.1139/t08-031

Channeling during settling and self-weight consolidation of cohesive sediments

2008· article· en· W2096449471 on OpenAlexvenueno aff
Soonkie Nam, Marte Gutierrez, Panayiotis Diplas

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersOffice of Naval ResearchNational Science Foundation
KeywordsConsolidation (business)SettlingFlocculationGeotechnical engineeringCoalescence (physics)SlurryGeologyMaterials sciencePore water pressureMineralogyComposite materialChemical engineeringEnvironmental scienceEngineeringEnvironmental engineeringPhysics

Abstract

fetched live from OpenAlex

As a part of the settling and self-weight consolidation of fine-grained materials, a common but not widely recognized phenomenon randomly occurs, namely channeling or the formation of narrow vertical paths or “channels.” Channel formation can have important effects on the microstructure, consolidation, and shear strength characteristics of newly formed sediments. However, only a few studies have been performed on channeling. The causes of channel formation and the factors affecting it are still not fully understood. This paper presents the results of an experimental study of channel formation during settling and self-weight consolidation in fine-grained materials. Four types of fine-grained materials and different slurry concentrations were tested using large and small settling columns, and channel formation was observed using a high-resolution digital camera. The results indicate that the primary mechanism for channel formation is the coalescence of small-scale discontinuities between clusters of soil particles formed by flocculation. The stronger the degree of flocculation, the more prevalent is the formation of channels. Pore pressure measurements show the effects of channels in dissipating excess pore pressures in sediments undergoing self-weight consolidation.

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 score0.135
Threshold uncertainty score0.556

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 integrity0.0000.000
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 head0.183
Teacher spread0.175 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

Explore more

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