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Record W4392520703 · doi:10.1061/9780784485347.029

Implementation of a New Strain Softening Constitutive Model in the Material Point Method for the Simulation of Retrogressive Failure in Sensitive Clays

2024· article· en· W4392520703 on OpenAlexaff
Zinan Ara Urmi, Ali Saeidi, Alba Yerro, Rama V. P. Chavalli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsConstitutive equationSofteningMaterial point methodPoint (geometry)Strain (injury)Computer scienceMaterials scienceGeotechnical engineeringGeologyStructural engineeringFinite element methodEngineeringComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

Sensitive clays experience significant strain-softening behavior, that is, when subjected to large strains. They disintegrate into a remolded liquid with diminutive shear strength. When a slope begins to fail, the remolded clay keeps moving away from its original position causing subsequent failures, resulting in catastrophic aftermath. The capability to reproduce realistic strain-softening characteristics in the constitutive soil model is necessary for more accurate numerical slope analyses in sensitive clays. This paper illustrates a simple yet practical constitutive model specially developed for simulating the strain-softening behavior of sensitive clays. The model is then implemented in Anura3D, an open-source software that uses the material point method to simulate large deformations. The model is tested using the failure of a previously occurred retrogressive failure in sensitive clay. Finally, the model’s predictions have been compared with the actual post-failure run out of the landslide, and the results show that the model is reasonable and practical.

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: none
Teacher disagreement score0.903
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.341
Teacher spread0.321 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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