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Record W2061863234 · doi:10.1139/t05-002

A pore-network model for hydromechanical coupling in unsaturated compacted clayey soils

2005· article· en· W2061863234 on OpenAlexvenueno aff
Paul Simms, Ernest K. Yanful

Bibliographic record

VenueCanadian Geotechnical Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic conductivitySoil waterGeotechnical engineeringWater retention curvePore water pressureDegree of saturationSuctionWater retentionMaterials scienceVoid ratioEffective stressSaturation (graph theory)Soil scienceGeologyThermodynamicsMathematics

Abstract

fetched live from OpenAlex

The behaviour of deformable unsaturated soils is difficult to characterize with simple relationships. Unsaturated hydraulic properties, namely the soil-water characteristic curve and the hydraulic conductivity function, are dependent on both volume change and degree of saturation, whereas volume change itself often cannot be related to a single stress variable but must be described by independent functions of mechanical loading and suction. It is proposed that a means to obtain these functions is through pore-network modelling, by which it is possible to integrate the phenomena of drainage and volume change and the different effects of suction and mechanical loading. The implementation of a two-dimensional pore-network model is described. A simple algorithm for individual pore volume change is adopted. The model uses pore-size distributions measured by mercury intrusion porosimetry to initially generate the simulated pore grid. Predictions of soil-water characteristic curves, void ratio versus suction curves, pore-size distributions at specific suctions, unsaturated hydraulic conductivity, and compression curves are compared with measured values from two compacted clayey soils.Key words: unsaturated soil, volume change, pore network, soil-water characteristic curve, unsaturated hydraulic conductivity, compacted clay.

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 categoriesMeta-epidemiology (narrow)
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 score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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.

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

Citations88
Published2005
Admission routes1
Has abstractyes

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