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Record W4313214323 · doi:10.1061/ijgnai.gmeng-7853

Analysis of One-Dimensional Consolidation for Double-Layered Soil with Non-Darcian Flow Based on Continuous Drainage Boundary

2022· article· en· W4313214323 on OpenAlexaff
Mengfan Zong, Wenbing Wu, M. Hesham El Naggar, Guoxiong Mei, Yi Zhang

Bibliographic record

VenueInternational Journal of Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsWestern University
Fundersnot available
KeywordsConsolidation (business)DrainageGeotechnical engineeringPermeability (electromagnetism)CompressibilityPore water pressureGeologyBoundary value problemSoil scienceMechanicsMathematicsChemistry

Abstract

fetched live from OpenAlex

The boundary drainage performance controls the rate of pore water discharge in the soil and plays an important role in the prediction of soil consolidation and settlement. Based on a continuous drainage boundary that can reflect the change of boundary drainage performance with time, a one-dimensional consolidation model of double-layered soil considering non-Darcian flow is established. The finite-difference method and semianalytical method are used to solve the consolidation equation, and the reliability of the two methods is verified by comparing with existing solutions. Based on the proposed solution, the consolidation behaviors of the double-layered soil are explored in depth through a systematic parametric study. The results show that, if the time effect of drainage boundary and the influence of non-Darcian flow are ignored, the estimated consolidation rate is relatively fast in the whole consolidation stage. The non-Darcian flow has a greater influence on soil consolidation under the continuous drainage boundary condition compared with that under the traditional drainage boundary condition. The consolidation rate of the foundation can be improved by appropriately increasing the permeability of the underlying soil layer or decreasing the compressibility of the underlying soil layer.

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 score0.257
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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