Insights into the Transport and Fragmentation Characteristics of Earthquake-Induced Rock Avalanche: Numerical Study
Bibliographic record
Abstract
The earthquake-induced rock avalanche in the Tangjia Valley was the most notable geological disaster triggered by the Lushan earthquake in 2013. In order to investigate the transport kinematics and depositional mechanism of this catastrophic landslide, a 2D discrete element model was developed and calibrated using field data. The model was then used to analyze the seismic response and mass transport process of a natural slope. The slope response to earthquake was numerically studied focusing on crack initiation, propagation, and coalescence within the rock mass. The mass movement and accumulation process were interpreted in terms of evolution of stress and solid fraction, kinematic behavior, and energy conversion. During the mass transport process, the slope was fragmented progressively due to intense shearing, allowing a basal layer of gradually fining solid particles to be generated with simultaneous occurrence of violent collisions, increase in particle kinematic activities, and the reduction of solid concentration. To further study this deformation process, fragment size distributions and fractal dimensions were described by Weibull distribution and power-law function, respectively. This statistical analysis reveals that dynamic disintegration continuously operates with the increasing runout distance. It is also found that the distribution of the fragment shapes becomes stable as the avalanche loses its momentum and deposition starts in the runout area. The proposed framework for the analysis of rock avalanches can be used to understand the physics of similar geological hazards.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".