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Record W4402790557 · doi:10.1139/cgj-2024-0145

Analytical solution for one-dimensional chemo-hydro-mechanical coupled consolidation under time-dependent loading in buffer clay layer

2024· article· en· W4402790557 on OpenAlexvenueno aff
Xibin Li, Yanghai Shen, Xiangyang Yu, Zhiqing Zhang, Wenjie Zhang

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsGeotechnical engineeringConsolidation (business)Buffer (optical fiber)Layer (electronics)GeologyMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

An analytical method is developed for solving the coupled chemo-hydro-mechanical consolidation in a clay buffer layer under time-dependent loading. The coupled governing equations for the chemo-hydro-mechanical process are established in the time and spatial domain first. Then, the governing equations are decoupled into two partial differential equations by introducing two variables. The analytical solutions corresponding to ramp and exponential loadings are finally derived based on the initial and boundary conditions. The developed analytical solutions are verified via comparing with the numerical results simulated by COMSOL Multiphysics. Based on the developed solutions, selected parametric study is carried out to investigate the influence of major parameters and hydraulic boundary conditions on the contaminants transport and pore water pressure dissipation in buffer clay layer. The results show that the major parameters have effects on the generation and dissipation of pore pressure, while only effective coefficient of diffusion, coefficient of ultrafiltration, and relative change of total density of pore liquid significantly affect the contaminant migration process. Compared with a single drainage boundary, the pore pressure dissipation and contaminant migration in a double drainage soil layer are much faster. The longer the loading time of mechanical loading, the more significant the negative pore pressure caused by the concentration gradient.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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