Multi-scale modelling of geomechanical behaviour using the Voronoi cell finite element method (VCFEM) and finite-discrete element method (VCFEM-DEM)
Bibliographic record
Abstract
The present work applies the hybrid Voronoi cell finite element method (VCFEM) within geomechanics. Coupled seepage and deformation analysis using the VCFEM incorporating body forces allows accurate analysis of earth dams. The development of a novel approach for simulating granular material behaviour using the combined finite-discrete element method (VCFEM-DEM) provides new insights into strain localization in granular materials. Chapter 1 provides background including summary literature reviews for all concepts in the title including seepage analysis, micromechanical and continuum mechanics theory, Voronoi diagrams, finite elements (FEM), discrete elements (DEM) and combined FEM-DEM. Chapter 1 concludes by detailing the contributions of the present work. Chapter 2 presents the VCFEM for seepage analysis. The numerical examples include an investigation of mesh sensitivity and a comparison of conforming shape functions. Polygonal elements with more than four nodes show a decrease in mesh sensitivity in free surface problems, compared with four-node quadrilateral elements. The choice of conforming shape function within the VCFEM analysis did not affect the results. Chapter 3 formulates and applies the VCFEM-DEM, showing that strain localization effects in granular materials are important at all scales. The VCFEM-DEM captures shear banding in biaxial compression tests, demonstrating that global shear strains and inhomogeneities in the shear stress field present after consolidation are early precursors to the failure mode. At the field scale, strain localization can lead to significant non-uniformity in subsurface stress distribution owing to self-weight. Chapter 4 presents the coupled VCFEM for seepage and deformation. A practical example of the design of an earth dam demonstrates the application of general body forces within a hybrid formulation, notably lacking in the literature. Chapter 5 concludes by summarizing the key observations of the present work, and providing direction for future research. The Appendix provides additional details related to numerical integration within the VCFEM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".