Uncertainty Analysis and Quantification of Rainfall-Induced Slope Instability in Fine-Grained Clayey Soils
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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