Hazard zoning of individual landslide-debris avalanches considering complex 3D spatial variability by random material point method
Bibliographic record
Abstract
Landslide-debris avalanche poses a serious threat to adjacent infrastructure and human safety. Due to their complex geological structures and depositional characteristics, natural slope soils often exhibit spatial variability, rotated anisotropy, and nonstationarity, making the prediction of slope post-failure behavior particularly challenging. These soil properties significantly impact slope stability and landslide movement. However, the influences of three-dimensional (3D) stratification and nonstationarity on consequences and hazard zones of the individual landslide remain underappreciated and relatively unexplored. This study proposes graphics processing unit (GPU) accelerated random generalized interpolated material point method for hazard zone assessment of the individual landslide that incorporates the 3D autocorrelation structural rotation, discretization of cross-correlated non-Gaussian random fields, modeling of nonstationary random field, landslide movement modeling, and estimation of exceedance probability surface. This innovative method is designed to effectively evaluate post-failure behaviors of slopes and hazard zones of the individual landslide under complex geological conditions. The findings demonstrate that considering the 3D spatial variability of soil is crucial for accurate individual landslide hazard assessments, as neglecting this variability leads to underestimation of potential hazard zones. This study establishes an improved framework for the hazard assessment of individual landslide and enhances our understanding of post-failure behavior of heterogeneous slopes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".