Landslide hazard assessment by smoothed particle hydrodynamics with spatially variable soil properties and statistical rainfall distribution
Bibliographic record
Abstract
Rainfall-induced landslides have caused significant damage to structures and casualties in the past decades, and it is of great importance to assess the post-failure behavior of slopes. This study proposes a probabilistic framework to evaluate the hazards associated with landslide runout arising from loose-fill slope failures. The failure process is simulated by the smoothed particle hydrodynamics (SPH) method, which is capable of capturing large deformations of landslides. The shear strength parameters of the soils are modeled as random variables, and random field simulations are performed to explore the effects of soil variability on the runout distance. In addition, the uncertainty in rainfall characteristics is represented by the Gumbel distribution, with the ensuing rainfall infiltration simulated in multiple seepage analyses to obtain pore pressure profiles in the slope, which are then adopted as initial conditions for the SPH method. Combining these various sources of uncertainty, the hazard factors indicating the risks for nearby structures are quantified based on the response uncertainty in landslide runout distances. To demonstrate this framework, the hazard levels associated with two typical layouts of loose-fill slopes are evaluated, and the results may serve as risk zoning indicators for adjacent developments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".