A new continuum dynamic model for the analysis of extremely rapid landslide motion across complex 3D terrain
Bibliographic record
Abstract
Runout analysis, the prediction of landslide motion and its effects, is an essential component of landslide risk assessment. A new continuum dynamic model has been developed for the runout analysis of extremely rapid, flow-like landslides, including rock avalanches, debris avalanches, debris flows and flow slides. The new model, DAN3D, is a 3D extension of the existing 2D model DAN. It uses a meshless, Lagrangian numerical method adapted from Smoothed Particle Hydrodynamics to discretize and solve the depth-averaged equations of motion for an "equivalent fluid", a hypothetical material governed by simple rheological relationships. The required rheological parameters, rather than measured, are calibrated through back-analysis of real landslide case studies. The key capabilities of the model include: 1) the ability to simulate motion across complex 3D terrain without the need to input a pre-defined path direction or width and without introducing problems due to mesh distortion; 2) the ability to simulate strain-dependent, non-hydrostatic, anisotropic internal stresses due to 3D deformation of material with internal shear strength; 3) the ability to simulate mass and momentum transfer due to entrainment of path material; 4) the ability to simulate variations in rheology along the path and within the landslide; and 5) efficient and simple operation. The model outputs the simulated spatial distribution of hazard intensity parameters, including flow velocity and depth, which are required for delineating the potential impact area, estimating the vulnerability of elements within this area and designing protective measures. It has been tested using both analytical and experimental methods, and its general behaviour has been demonstrated using a series of simple parametric analyses. The model has also been applied at full-scale to the simulation of a wide variety of landslide types. These back-analyses form the basis for a more thorough calibration, but some useful patterns have already emerged. This experience has been applied in practice to landslide runout prediction, although so far only in a parametric way. With continued back-analysis of real cases and the development of a probabilistic approach to forward-analysis, true landslide runout prediction, including quantification of uncertainty, should eventually be possible using the new model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".