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Record W2160910050 · doi:10.1139/t05-066

Pore-water pressure and water volume change of an unsaturated soil under infiltration conditions

2005· article· en· W2160910050 on OpenAlexvenueno aff
Inge Meilani, Harianto Rahardjo, Eng‐Choon Leong

Bibliographic record

VenueCanadian Geotechnical Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersNational Science BoardNanyang Technological University
KeywordsInfiltration (HVAC)Geotechnical engineeringPore water pressureSoil waterShearing (physics)SuctionMaterials scienceSoil scienceGeologyComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Triaxial shearing–infiltration tests were conducted to study the pore-water pressure and volume change of unsaturated soils subjected to infiltration conditions. A modified triaxial apparatus with three Nanyang Technological University (NTU) mini suction probes along the specimen height was used for the experimental program. Elastic moduli were obtained for the soil structure with respect to changes in net confining pressure (E) and matric suction (H). Water volumetric moduli associated with changes in net confining pressure (E w ) and matric suction (H w ) were also obtained from the shearing–infiltration tests. Water volumetric strain and pore-water pressure during the shearing–infiltration tests were computed based on volume change theory. This paper presents the significance of obtaining the parameter H w from an appropriate scanning curve of a soil-water characteristic curve (SWCC) for the computation of water volumetric strain and pore-water pressure changes during a shearing–infiltration test. The appropriate scanning curve should be obtained from the wetting curve of the SWCC at the matric suction where the infiltration test commences.Key words: infiltration, matric suction, triaxial, unsaturated soils, pore-water pressure, water volume change.

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 score0.238
Threshold uncertainty score0.469

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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

Explore more

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