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Record W2578347135 · doi:10.1139/cgj-2016-0297

Water retention model for compacted bentonites

2017· article· en· W2578347135 on OpenAlexvenueno aff
A. Dieudonne, Gabriele Della Vecchia, Robert Charlier

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureFonds De La Recherche Scientifique - FNRS
KeywordsWater retentionBentoniteGeotechnical engineeringAggregate (composite)SuctionVolume (thermodynamics)CompactionRange (aeronautics)Soil waterMaterials scienceEnvironmental scienceGeologyPetroleum engineeringSoil scienceEngineeringComposite materialMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The water retention behaviour of compacted bentonites is strongly affected by multi-physical and multi-scale processes taking place in these materials. Experimental data have evidenced major effects of the material dry density, the imposed volume constraints, and the soil fabric. This paper presents a new water retention model accounting for proper retention mechanisms in each structural level of compacted bentonites, namely adsorption in the intra-aggregate pores and capillarity in the inter-aggregate ones. The model is calibrated and validated against experimental data on different bentonite-based materials, showing good capabilities in capturing the main features of the behaviour. The model is able to reproduce experimental data on compacted bentonites over a wide range of suction values, within a unified framework, and using a limited number of parameters. Some of the parameters introduced are shown to take approximately the same value for several bentonites, providing a significant basis for preliminary design when dedicated experiments are missing.

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 score0.204
Threshold uncertainty score0.414

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 studies0.0010.000
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 head0.027
GPT teacher head0.229
Teacher spread0.201 · 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

Citations84
Published2017
Admission routes1
Has abstractyes

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