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Three-dimensional slope stability study using a Coupled Eulerian-Lagrangian method

2020· article· en· W3089584301 on OpenAlexaboutno aff
Fang-Cing Liu, Chih-Hsuan Liu, Ching Pui Hung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodMathematicsStability (learning theory)Slope stability analysisGeometryPolygon meshRegular polygonSlope stabilityMathematical analysisPhysicsGeologyComputer scienceGeotechnical engineeringThermodynamics

Abstract

fetched live from OpenAlex

<p>  In slope stability analysis, two-dimensional (2D) analysis techniques are usually applied due to its simplicity and extensive applicability. Given that slope failures are three-dimensional (3D) in nature, especially in the slope with complex geometry, a 3D slope stability analysis could lead to more reasonable results [1]. In slope stability analyses, limit equilibrium method (LEM) and finite element method (FEM) are widely used. Note that LEM only satisfies equations of statics and does not consider strain and displacement compatibility; FEM may encounter significant mesh distortion during large deformations where convergence difficulty and the analysis may be terminated before the slope reaches failure [2]. In the study, a Coupled Eulerian-Lagrangian (CEL) method, which allows materials to flow through fixed meshes regardless of distortions, was utilized to investigate 3D slope stability [3]. Validation of the numerical modeling was first presented using a typically assumed 3D slope. After the validation, various types of slopes (i.e. turning corners, convex- and concave-shaped surfaces) with various boundary conditions (unrestrained, semi-restrained, and fully restrained) are carefully conducted to examine the 3D slope stability. It is anticipated the 3D analyses can shed some light on the slope stability analysis with extreme or complex geometry cases and provide more reasonable results.</p><p> </p><p>REFERENCE</p><ol><li>T.-K. Nian, R.-Q. Huang, S.-S. Wan, and G.-Q. Chen (2012): Three-dimensional strength-reduction finite element analysis of slopes: geometric effects. Canadian Geotechnical Journal, 49: 574–588.</li> <li>C. Hung, C.-H. Liu, G.-W. Lin and Ben Leshchinsky (2019): The Aso-Bridge coseismic landslide: a numerical investigation of failure and runout behavior using finite and discrete element methods. Bulletin of Engineering Geology and the Environment. doi: 10.1007/s10064-018-1309-3.</li> <li>C. Han. Lin, C. Hung and T.-Y. Hsu (2020): Investigations of granular material behaviors using coupled Eulerian-Lagrangian technique: From granular collapse to fluid-structure interaction. Computers and Geotechnics (under review).</li> </ol><p> </p><p> </p>

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 score0.621
Threshold uncertainty score0.803

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)0.0010.000

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 head0.034
GPT teacher head0.255
Teacher spread0.221 · 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".

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Citations0
Published2020
Admission routes1
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