Hydromechanical modeling of granular soils considering internal erosion
Bibliographic record
Abstract
This paper attempts to formulate a coupled practical model in the framework of continuum mechanics to evaluate the phenomenon of internal erosion and its consequences on the mechanical behavior of soils. For this purpose, a four-constituent numerical approach has been developed to describe the internal erosion process. The detachment and transport of the fine particles have been described by a mass exchange formulation between the solid and fluid phases. The stress–strain relationship of the soil is represented by a nonlinear incremental model. Based on experimental data, this constitutive model has been enhanced by the introduction of a fines content–dependent critical state, which allows accounting for the influence of fines on soil deformation and strength. The applicability of the practical approach to capture the main features of the internal erosion process and its impact on the mechanical behavior of the eroded soil have been validated by comparing numerical and experimental results of internal erosion tests on Hong Kong completely decomposed granite (HK-CDG) mixtures, which demonstrated that the practical model was able to reproduce, with reasonable success, the experimental data. Furthermore, the influence of the stress state, the initial soil density, and the initial fraction of fines have been analyzed through numerical simulations using the proposed model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".