Torrential hazard assessment using a debris-flow runout model. The case of the Faucon stream
Bibliographic record
Abstract
Debris-flows are able to transport large quantities of sediment downslope, producing complex distributions of deposits\nand eroded surfaces along their flow path. Incorporation of surficial deposits during a debris-flow may change the mechanical\nbehaviour of the flow. This paper presents the results of various rheological tests and numerical modelling for assessing torrential risk scenarios in the Faucon torrential stream where a debris-flow occurred in 1996, after a severe thunderstorm over the catchment basin\nand the breaking of a natural dam. Grain-size distribution and petrographic analysis have shown that this debris-flow can be\ncharacterized as a granular then a muddy debris-flow. Rheological tests using either a parallel-plate rheometer, a coaxial rheometer,\nslump tests, or a inclined plane were carried out on several samples. Results have shown that the flow behaviour could be described\nby a Heschel-Bulkley constitutive equation. Rheological response of several natural suspensions collected from quaternary deposits\nwere also investigated. In order to model the runout of the flow, we used the BING code. Model describes well the influence of each\ntype of sediment on the behaviour (runout distance, deposit thickness) of the flow, neither the velocities were overestimated. Different risk scenarios are tested and discussed. / Les laves torrentielles sont capables de transporter de grandes quantités de sédiment, produisant des distributions complexes de surfaces érodées ou de dépôt le long de leur chenal d`écoulement. L`incorporation de dépôts de superficiels au cours de l`écoulement de la lave torrentielle est susceptible de faire varier les propriétés mécaniques du matériau de cette dernière. Le présent papier présente les résultats de différents tests rhéologiques et de modélisations numériques pour l`établissement de scénarios de risque torrentiel sur le torrent du Faucon qui a connu un événement de lave torrentielle en 1996, après un violent orage sur son bassin versant et la rupture d`un barrage naturel. La courbe granulométrique et l`analyse pétrographique ont montré que cette lave torrentielle peut être caractérisée comme une lave torrentielle granulaire puis comme une lave torrentielle à matrice boueuse. Des tests rhéométriques ont été menés sur plusieurs échantillons en utilisant un rhéomètre plan-plan, un rhéomètre coaxial, un slump test ou un test au plan incliné. Les résultats ont montré que la loi de comportement du matériau pouvait être décrite par un modèle de Herschel-Bulkley. La caractérisation rhéologique de plusieurs suspensions naturelles, collectées sur dans des dépôts quaternaires, a également été menée. Dans le but de simuler la propagation de l`écoulement, nous avons utilisé le code BING. Le modèle rend bien compte de l`influence de chaque type de sédiment sur le comportement (distance parcourue, épaisseur de dépôt) de l`écoulement, toutefois, les vitesses étaient surestimées. Différents scénarios de risque sont testés et discutés.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".