Impact dynamics of flow-type landslides on V-shaped diversions: combined numerical and experimental approach
Bibliographic record
Abstract
In sparsely populated and rural areas, government agencies propose the installation of V-shaped diversions to mitigate flow-type landslides. In contrast to rigid barriers, diversions are small, easy-to-construct, and cost-effective. Nonetheless, the impact dynamics of flow-type landslides against diversion structures remains unclear and hinders the development of scientific-based design guidelines. Diversion angles that are too large decelerates the flow and causes an overspill. In contrast, diversion angles that are too small results in long walls that are not feasible to construct. In this study, laboratory-scale flume experiments modeling the impact of dry sand against diversions are conducted. The experimental data are used to validate a coupled material point method and discrete element method numerical model. The numerical model is used to conduct a parametric study to investigate the effects of post-impact flow. The transition from attached oblique to detached bow shocks occurs with decreasing inflow and increasing diversion angle, causing the largest flow runup height at the diversion side rather than the diversion apex. It is proposed to design diversions based on bow and oblique shock mechanisms. Design charts that consider the competing effects between deflected and accumulated state are proposed. The newly proposed analytical model for predicting deflection height to mitigate overspill shows close agreement with experimental results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".