Simple-shear and direct-shear behaviours of grass-rooted soils
Bibliographic record
Abstract
Root reinforcement to soil has been commonly quantified by direct-shear tests (DSTs). Despite the simplicity and popularity, this testing method is often criticised for imposing a predefined shear plane, unrealistically representing the in situ stress conditions. Suitability of using DST to characterise the mechanical properties of rooted soils has never been discussed before. This study aims to investigate the differences in the shearing behaviour and stress–dilatancy relationships of rooted soils obtained from simple-shear tests (SSTs) and DSTs. A new stress–dilatancy relationship for rooted soils was derived to explain the additional soil dilatancy contributed by roots upon shearing. The measurements and prediction consistently revealed that the DSTs substantially overestimated the cohesion (by 100%) and peak friction angle (by 15%) of rooted soils, compared to SSTs. This phenomenon is due to the much greater principal stress rotation in the DSTs due to the undesirable stress concentration near the forced horizontal shear plane, causing virtual increases in root reinforcement. Cautions should be taken when using DST to quantity root reinforcement to prevent unsafe engineering design. Predictions made by the validated model showed that the effects of root-induced dilatancy and the associated increase in peak friction angle were more prominent for shallow rooted soils.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".