Machine learning-aided reliability analysis of rainfall-induced landslide of root-reinforced slopes
Why this work is in the frame
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Bibliographic record
Abstract
Estimating the failure probability of rainfall-induced landslides is often challenging as the triggering mechanism is influenced by a number of parameters whose uncertainty is difficult to quantify and, in practice, is neglected. The reinforcing effect of vegetation on natural slopes adds to the complexity of the stability analysis. In this study, we present the application of a coupled hydro-mechanical model for the effect of plant roots on soil shear strength. First, a deterministic approach is adopted. Then, a reliability analysis of a root-reinforced slope subjected to rainfall is performed by considering the inherent variability of the soil and root properties. The probability of failure is estimated with machine learning surrogate models, which approximate the nonlinear relationship between constitutive parameters and slope displacements at different time steps. The machine learning algorithms are trained on a small dataset. The extreme gradient boosting is the best-performing algorithm with R 2 ≥ 0.975 and is then employed to estimate the probability of failure on a larger dataset of one million datapoints with higher accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it