Three-dimensional material point method modeling of runout behavior of the Hongshiyan landslide
Bibliographic record
Abstract
This study presents a field-scale simulation of the Hongshiyan landslide in China. It uses an advanced numerical approach (material point method (MPM)) and a constitutive model (the Drucker–Prager model + μ(I) rheological relation) for the three-dimensional (3D) simulation. The performance of the developed MPM model is validated with laboratory-scale experimental data on granular collapse before being applied to field-scale analyses. ArcGIS data are used to create a 3D MPM model of the soil body with complicated geometry. Although the developed model can describe the multiple phases of granular flow, it focuses on the runout behavior of the landslide in this work. The landslide is assumed to have occurred suddenly due to an earthquake, and global sudden failure rather than progressive failure is modeled. The MPM simulation results match reasonably well with the measured post-earthquake topography (e.g., deposit height of about 120 m and stretch length of about 900 m in the river) and landslide duration of about 1 min. The velocity of the sliding mass increases rapidly during flow, especially in the first 20 s. The velocity profiles along the depth direction at different locations of the sliding body exhibit an exponential distribution similar to that of a Bagnold-type profile, indicating that the sliding body is fully mobilized. The rate-dependent dissipation parameter β used in the model significantly influences the runout behavior (e.g., flow speed, velocity distribution, and deposit shape).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".