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Record W2095346684 · doi:10.1155/2012/153123

Electrokinetic Treatment for Model Caissons with Increasing Dimensions

2012· article· en· W2095346684 on OpenAlexafffund
Eltayeb Mohamedelhassan, Kevin Curtain, Matt Fenos, Kevin P. Girard, Anthony Provenzano, Wesley Tabaczuk

Bibliographic record

VenueAdvances in Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaLakehead University
KeywordsCaissonElectrokinetic phenomenaAnodeFoundation (evidence)Energy consumptionGeotechnical engineeringVoltageComputer scienceEnvironmental scienceMaterials scienceElectrodeGeologyEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Electrokinetic treatment has been known in geotechnical engineering for over six decades, yet, the technique is rarely used. This stems from the absence of design guidelines and specifications for electrokinetic treatment systems. An important issue that need to be investigated and understood in order to devise guidelines from experimental results is the effect of the foundation element size on the outcome of the treatment. Also important is determining the optimum distance between the electrodes and estimating the energy consumption prior to treatment. This experimental study is a preliminary step in understanding some of the issues critical for the guidelines and specifications. Four model caissons with surface areas between 16000 and 128000 mm 2 were embedded in soft clayey soil under water and treated for 168 hr with a dc voltage of 6 V. From the results, a distance between the anode (model caisson) and the cathode equal 0.25 times the outside diameter of the model caisson was identified as optimum. Relationships between the surface area and axial capacity of the model caisson and the surface area and energy consumption were presented. The equations can be used to preliminary estimate the load capacity and the energy consumption for full-scale applications.

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: none
Teacher disagreement score0.730
Threshold uncertainty score0.890

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 head0.008
GPT teacher head0.237
Teacher spread0.230 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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