An Effective Stress Equation for Unsaturated Granular Media in Pendular Regime
Bibliographic record
Abstract
The mechanical behaviour of a wet granular material is investigated through a micromechanical analysis of force transport between interacting particles with a given packing and distribution of capillary liquid bridges. A single effective stress tensor, characterizing the tensorial contribution of the matric suction and encapsulating evolving liquid bridges, packing, interfaces, and water saturation, is derived micromechanically. The physical significance of the effective stress parameter (χ) as originally introduced in Bishop’s equation is examined and it turns out that Bishop’s equation is incomplete. More interestingly, an additional parameter that accounts for surface tension forces arising from the so-called contractile skin emerges in the newly proposed effective stress equation. Therefore, a so-called capillary stress is introduced which is shown to have two contributions: one emanating from suction between particles due to air-water pressure difference, and the second arising from surface tension forces along the contours between particles and water menisci. It turns out that the capillary stress is anisotropic in nature as dictated by the spatial distribution of water menisci, particle packing and degree of saturation, and thus engenders a meniscus based shear strength that increases with the anisotropy of the particle packing and the degree of saturation. The newly proposed effective stress equation is analyzed with respect to packing, liquid bridge distribution and strength issues. Finally, discrete element modelling is used to verify the micromechanical aspects of the proposed effective stress equation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".