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Record W4319338464 · doi:10.1139/cgj-2022-0601

A multi-component model for expansive soils with different mineral compositions

2023· article· en· W4319338464 on OpenAlexvenueno aff
Jiapei Du, Annan Zhou, Xiaoshan Lin, Dilan Robert, Filippo Giustozzi

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsExpansive claySwellingGeotechnical engineeringSoil waterExpansiveStrain (injury)Deformation (meteorology)Clay mineralsGeologyMaterials scienceSoil scienceMineralogyComposite materialCompressive strength

Abstract

fetched live from OpenAlex

The accurate prediction of swelling deformation and(or) swelling pressure if confined for expansive soils is essential for a better design and construction of a geotechnical structure that is built on or in an expansive soil. In this paper, we proposed a new method to quantify the swelling strain and(or) swelling pressure of expansive soils by considering the mineralogical components and their different swelling properties. The proposed multi-component model employs a tandem combination of different sub-molecular oedometers for different minerals, and the total swelling strain of soil was calculated by integrating the swelling strain of each mineral. A linear relationship between potential energy and swelling strain is identified based on molecular dynamics’ simulation, which is then incorporated into the diffuse double-layer theory to calculate the relationship between confining pressure and swelling strain for each mineral. The numerical model was then validated by experimental results from the literature, which shows a good agreement with the experimental data in the literature. This model provides a cost-effective way to estimate the swelling behavior of expansive soil.

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 score0.228
Threshold uncertainty score0.574

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 head0.022
GPT teacher head0.224
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

Citations21
Published2023
Admission routes1
Has abstractyes

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