Influences of root-induced soil suction and root geometry on slope stability: a centrifuge study
Bibliographic record
Abstract
Soil bioengineering using vegetation has been recognised as an environmentally friendly solution for shallow slope stabilization. Plant transpiration induces suction in the soil, but its effects on slope stability are often ignored. This study investigates the influences of transpiration-induced suction and mechanical reinforcement of different root geometries (i.e., tap- and heart-shaped) to the slope stability subjected to an intense rainfall with an intensity of 70 mm/h (prototype scale; corresponding to a return period of 1000 years), via centrifuge modelling. New model roots that have scaled mechanical properties close to real roots were used to simulate transpiration-induced suction in the centrifuge. Transient seepage analyses were performed using SEEP/W to back-analyse the suction responses due to transpiration and rainfall. Subsequently, the back-analysed suction was used to assess the factor of safety of the slopes using SLOPE/W. It is revealed that heart-shaped roots provided greater stabilization effects to a 60° clayey sand slope than tap-shaped roots. The heart-shaped roots induced higher suction, leading to 14% reduction of rainfall infiltration and 6% increase in shear strength. Although transpiration-induced suction in a 45° slope was reduced to zero after the rainfall, mechanical root reinforcement was found to be sufficient to maintain slope stability.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".