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Record W1855687014 · doi:10.1139/cgj-2013-0263

Discrete-continuum analysis of monotonic pile penetration in crushable sands

2014· article· en· W1855687014 on OpenAlexvenueno aff
Jianfeng Wang, Budi Zhao

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersCity University of Hong KongNational Science Foundation
KeywordsBreakagePileDiscrete element methodGeotechnical engineeringCentrifugeParametric statisticsAgglomerateVoid (composites)Penetration (warfare)Structural engineeringMonotonic functionVoid ratioEngineeringMechanicsMaterials scienceComposite materialMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents numerical results from a two-dimensional discrete element method (DEM) simulation study on the monotonic pile installation in crushable sands. The particle breakage was included in the model by setting a crushable zone around a pile that was filled with parallel-bonded agglomerates. In the other part of the model, rigid, unbreakable particles are used to minimize the computational cost. Parametric studies were carried out to examine the effects of initial in situ vertical stress, soil void ratio, and particle crushability on the penetration resistance behavior. The validity of the DEM model was examined by comparing the simulation data with published results from laboratory centrifuge and calibration chamber tests on model pile installation. A variety of DEM analysis techniques were employed to make a detailed discrete-continuum study on the pile penetration mechanisms, particularly on the particle breakage-related soil mechanics incurred during the penetration process. Simulation results show that the in situ stress and particle breakage are the two competing factors dominating the tip resistance behavior. The underlying mechanism was elucidated through the analysis of stress and strain paths derived from pre-assigned sampling windows and their full-field distributions.

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)0.001
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 score0.239
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

Explore more

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