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Record W4386509628 · doi:10.1139/cgj-2023-0194

Discrete element analysis of geogrid–aggregate interface shear behavior under cyclic normal loading

2023· article· en· W4386509628 on OpenAlexvenueno aff
Yaqiong Wang, Shi‐Jin Feng

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsGeogridMicroscale chemistryGeotechnical engineeringDiscrete element methodDirect shear testAggregate (composite)InterlockingMaterials scienceShear (geology)Finite element methodStructural engineeringShear stressAmplitudeMechanicsComposite materialGeologyReinforcementEngineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

The stability of a geogrid-stabilized structure affected by cyclic normal loading (CNL) is significant but has not been fully revealed. Using the discrete element method (DEM), the effect of CNL on the microscale mechanical responses (i.e., stress states, contact evolution, fabric deformation) of the geogrid–aggregate interface direct shear test is first investigated. The complex shear behaviors at the interface with normal cyclic excitation at different frequencies and amplitudes are simulated. The DEM model is able to capture the macroscopic dynamic shear laws at the geogrid–aggregate interface in a similar way to those tested experimentally. The detailed behavior of the aggregate interacting with the geogrid under CNL is investigated. Compared with the simulation under static normal loading (SNL), CNL makes the stabilized layer more prone to failure, which could be quantitively evaluated by analyzing the local shear strain and the interparticle interlocking level. Microscale studies on the load wave propagation process and the confinement zone indicate that the present method can provide an applicable tool for dynamic service assessment and reliable forecasting of the undesirable effect of CNL on a mechanically stabilized 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 categoriesMeta-epidemiology (narrow)
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 score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.232
Teacher spread0.222 · 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.

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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207