Impact force of granular flows on walls normal to the bottom: slow versus fast impact dynamics
Bibliographic record
Abstract
The devastating effects of natural hazards due to the propagation of mass flows, such as landslides, debris flows, and avalanches, can be avoided, or at least reduced, by placing protective barriers or catching dams in the runout zones. Such structures can store the whole mass and finally stop the flow before it may reach vulnerable infrastructures. Their design requires the modelling of the runup of granular flows on rigid walls and the induced impact force. In this study, careful attention is paid on how the incoming flow regime (either slow or fast flow) that takes place before the impact with the wall can drive the prevailing process at stake during the impact of the flow with the wall. Slow flows produce gentle pile-up of the mass behind the wall with gradually varied streamlines, while faster flows give rise to a granular jump traveling upstream. Two different analytic solutions are proposed and checked against recent small-scale laboratory tests by Ashwood and Hungr (2016), who investigated both slow and fast impact dynamics of granular flows against a wall. This allows to clarify the intertwinned relation between the incoming flow regime and the induced impact force, thus providing crucial information for the geotechnical engineers in charge of the design of mitigation structures against mass flows and mountain hazards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".