Stochastic Finite Element Analysis of Root-Reinforcement Effects in Long and Steep Slopes
Bibliographic record
Abstract
This article introduces a novel numerical scheme within the finite element method (FEM) to study soil heterogeneity, specifically focusing on the root–soil matrix in fracture treatments. Material properties, such as Young’s modulus of elasticity, cohesion, and the friction angle, are considered as randomly distributed variables. To address the inherent uncertainty associated with these distributions, a Monte Carlo simulation is employed. By incorporating the uncertainties related to material properties, particularly the root component that contributes to soil heterogeneity, this article provides a reliable estimation of the factor of safety, failure surface, and slope deformation, all of which demonstrate a progressive behavior. The probability distribution curve for the factor of safety (FOS) reveals that an increase in the root area ratio (RAR) results in a narrower range and greater certainty in the population mean, indicating reduced material variation. Moreover, as the slope angle increases, the sample mean falls within a wider range of the probability density curve, indicating an enhanced level of material heterogeneity. This heterogeneity amplifies the level of uncertainty when predicting the factor of safety, highlighting the crucial importance of accurate information regarding heterogeneity to enhancing prediction accuracy.
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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 | 0.001 |
| 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 | 0.001 | 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".