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Record W4412608611 · doi:10.1139/cgj-2024-0737

Electro-osmotic-assisted consolidating of undisturbed soft sensitive clay

2025· article· en· W4412608611 on OpenAlexvenueno aff
Mohamad Hanafi, Sofia Kiikkera, Nicolas Vibert, Matti Ristimaki, Leena Korkiala-Tanttu, Sanandam Bordoloi

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsnot available
FundersAalto-Yliopisto
KeywordsGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Electro-osmotic consolidation has been recognized as a potential pathway to quickly drain dredged sediments and highly compressible disturbed clay. However, its efficacy in consolidating sensitive undisturbed soft clays found in the Nordic regions are not present. Even before initiating these techniques in the field, laboratory assessment of the electro-osmotic assisted drainage rate, consolidation parameters of the clay and corrosion potential of the electrodes are essential information for engineers. This laboratory study investigates the effect of voltage (10, 20, and 30 V) on the consolidation parameters of undisturbed soft clays using only electro-osmosis and then a coupled approach (incremental loading with intermittent current). The efficacy of the exit hydraulic gradient (in m/s) increased by three-to-four orders regardless of the voltage used in the case of only electro-osmosis assisted consolidation. The electro-osmosis assisted consolidation allowed for consolidation up to 30% at in situ stresses (12–15 kPa) and within 2 h. Such rapid consolidation contrasts with 47% consolidation achieved when preloading is applied even at 1000 kPa, which in field would take place in months. When the coupled approach was utilized, even at 50 kPa of applied stress the consolidation improved by 30% even at 10 V. The coupled approach lowers the corrosion potential to negligible levels (≤0.2% by weight).

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)0.001
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: none
Teacher disagreement score0.675
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicElectrokinetic Soil Remediation TechniquesFrench-language works237,207