A sliding block model for the runout prediction of high-speed landslides
Bibliographic record
Abstract
A sliding block model is developed for predicting the runout of high-speed slide-like landslides, based on the analysis of the dynamic mechanism of high-speed landslides. This model adopts the limit equilibrium analysis approach and incorporates mass dynamics and soil deformation into the calculation of soil movements. The critical state is considered as the initial stress state, and the interslice forces are obtained considering the equivalence of deformation energy. It is possible, applying this model, to simulate the whole travel process of the soil mass from the onset of the landslide and to predict the travel speed of the soil mass and the hazard area of the landslide. Application of the model to the Sale Mountain landslide and the Tianshui Forging Machine Factory landslide in China gives reasonable results in comparison to the field observations. The calculated results illustrate that (i) the fundamental causes of the high speed are the decline of the resultant friction force during sliding and the rapid and large fall of the centre of gravity of the back part of the slide mass; and (ii) the slide mass generally moves as a whole during the middle sliding stage, and the front part of the slide mass is often in a state of relaxation, even extension, at the end of the movement.Key words: high-speed landslides, dynamic analysis, runout distance, block 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".