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Record W2154354722 · doi:10.1680/grim.2009.162.4.157

Electrokinetic strengthening of soft clay

2009· article· en· W2154354722 on OpenAlexafffund
Eltayeb Mohamedelhassan

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Ground Improvement · 2009
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrokinetic phenomenaAnodeGeotechnical engineeringMaterials scienceOverburden pressureElectrokinetic remediationShear strength (soil)ElectrodeComposite materialGeologyChemistrySoil waterSoil science

Abstract

fetched live from OpenAlex

The use of electrokinetic treatment to decrease the water content and increase the shear strength, preconsolidation pressure and axial load capacity of a laboratory-prepared soft clay soil was investigated. The tests were carried out in four identical electrokinetic cells. The cell has a volume capacity of 10 litres. A DC voltage of 10 V was applied in the tests investigating the water content, shear strength and preconsolidation pressure. A DC voltage of 5 V was used in the tests investigating the axial load capacity. The energy consumption for each test was 54 W h. The electrokinetic treatment decreased the water content across most of the cell with the lowest water content near the anode (32·8 ± 2% in comparison with 52·7 ± 2·7% in the control) and increased the undrained shear strength across the cell with the highest shear strength reported near the anode (62·5 ± 6·2 kPa in comparison with 6·3 ± 2·1 kPa in the control). Electrokinetic treatment increased the preconsolidation pressure across the cell with the maximum pseudo-preconsolidation pressure near the anode (91 kPa in comparison with pre-loaded surcharge pressure of 10 kPa). The axial load capacity of the foundation model after the treatment was 156 N when the foundation model was serving as the anode and 173 N when the model was not used as an electrode. The loss in the mass of the foundation model by corrosion was 4·7% for the former and 0·4% for the latter.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.753

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 head0.004
GPT teacher head0.192
Teacher spread0.187 · 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 designBench or experimental
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

Citations12
Published2009
Admission routes2
Has abstractyes

Explore more

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