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Record W2233788873 · doi:10.1139/cgj-2015-0381

Hydromechanical behaviour of unsaturated soil with different specimen preparations

2015· article· en· W2233788873 on OpenAlexvenueno aff
You Gao, De’an Sun, Annan Zhou

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsVoid ratioMaterials scienceSuctionCompactionGeotechnical engineeringComposite materialMercury intrusion porosimetrySiltOverburden pressurePorosityGeologyPorous medium

Abstract

fetched live from OpenAlex

The effect of different specimen preparation methods (compaction, reconstitution) on the hydromechanical behaviour and microstructure of soil was studied through a series of soil-water retention tests, triaxial tests, and mercury intrusion porosimetry (MIP) tests on an unsaturated clayey silt. Key findings from this experimental study include the following. (i) The air-entry value of the reconstituted specimen is higher than that of the compacted specimen with the same initial void ratio. (ii) The reconstituted specimen exhibits a unimodal pore-size distribution and the compacted specimen exhibits a bimodal pore-size distribution, according to results of the MIP tests. (iii) The soil-water characteristic curves and residual gravimetric water content of compacted and reconstituted specimens are almost the same in the high suction range. (iv) The void ratio of a reconstituted specimen decreases sharply with increasing suction value under an isotropic net stress (such as 20 kPa), while the void ratio of a compacted specimen shows a small change for the same suction change process. (v) Under the same net confining pressure, the shear strength of both compacted and reconstituted specimens increases with the suction increase. The shear strength of the reconstituted specimen is higher than that of the compacted specimen with the same density under the same suction and net confining pressure.

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 score0.055
Threshold uncertainty score0.568

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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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

Citations56
Published2015
Admission routes1
Has abstractyes

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