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Record W2113069497 · doi:10.1139/t10-019

Numerical simulations of stone column installation

2010· article· en· W2113069497 on OpenAlexvenueno aff
Jorge Castro, Minna Karstunen

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsColumn (typography)Geotechnical engineeringPore water pressureAnisotropyFinite element methodLateral earth pressureComputer simulationGeologyMechanicsStructural engineeringEngineeringConnection (principal bundle)Physics

Abstract

fetched live from OpenAlex

This paper describes the results of numerical simulations investigating the installation effects of stone columns in a natural soft clay. The geometry of the problem is simplified to axial symmetry, considering the installation of one column only. Stone column installation is modelled as an undrained expansion of a cylindrical cavity. The excess pore pressures generated in this process are subsequently assumed to dissipate towards the permeable column. The process is simulated using a finite element code that allows for large displacements. The properties of the soft clay correspond to Bothkennar clay, modelled using S-CLAY1 and S-CLAY1S, which are Cam clay–type models that account for anisotropy and destructuration. Stone column installation alters the surrounding soil. The expansion of the cavity generates excess pore pressures, increases the horizontal stresses of the soil, and most importantly modifies the soil structure. The numerical simulations performed allow quantitative assessment of the post-installation value of the lateral earth pressure coefficient and the changes in soil structure caused by column installation. These effects and their influence on stone column design are discussed.

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.199
Teacher spread0.192 · 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

Citations109
Published2010
Admission routes1
Has abstractyes

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