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Record W7108081295 · doi:10.1051/epjconf/202534007001

Liquefaction of crushable granular media: A multiscale numerical analysis

2025· article· en· W7108081295 on OpenAlexaff

Bibliographic record

VenueEPJ Web of Conferences · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsLiquefactionGranular materialShear (geology)Fragmentation (computing)Smoothed-particle hydrodynamicsMicrostructureMicromechanicsNumerical analysis

Abstract

fetched live from OpenAlex

Understanding liquefaction—the loss of shear resistance in granular materials under constantvolume shearing—is crucial for preventing landslides and geotechnical failures. This phenomenon typically occurs in water-saturated media during rapid undrained loading, such as seismic shocks. Liquefaction potential decreases with higher solid fraction and particle size polydispersity, but evolves if particle fragmentation occurs. We investigate the mechanics and microstructure of crushable, liquefiable granular materials through 2D undrained shear simulations. Results show that higher solid fractions and stronger particles delay liquefaction. Mechanical instabilities manifest as sharp drops in mean and deviatoric stresses, leading to resistance loss and fluid-like behavior. The redundancy number strongly correlates with shear resistance and contact network stability. At high solid fractions, grading upon fragmentation asymptotically approaches an ultimate state while maintaining stability. In contrast, looser samples exhibit earlier liquefaction, with fragmentation depending on particle strength. These findings highlight the critical role of particle strength in either mitigating or intensifying liquefaction.

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 score0.313
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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