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Record W2004675569 · doi:10.1139/t08-059

Simplified models of spreading flow of dry granular material

2008· article· en· W2004675569 on OpenAlexaffvenue
Oldrich Hungr

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFlow (mathematics)LandslideGeologyGeotechnical engineeringGranular materialMechanicsWork (physics)EngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Shallow-flow integrated numerical models of landslide and avalanche motion have recently experienced rapid development. An important aspect of model development is verification, to show that models are correct and reasonably robust in their application of basic physical principles. Most existing models have been verified against controlled laboratory experiments using dry granular material. In this article, it is shown that spreading flows such as the “dam-break” problem in frictional material pose problems for shallow-flow analysis. A series of dam-break laboratory experiments have been carried out with several different slope angles and bed materials. A model was then applied using four alternative assumptions regarding the distribution of earth pressure in the sliding mass. It was shown that assumptions commonly used in previous work produce very substantial errors (up to 200%) when applied to the prediction of dam-break runout in material with both internal and basal friction. A new model is proposed based on a simple modification of the well-known SH assumptions. This new model provides excellent agreement with experimental results, both for shallow avalanches and spreading flows. It represents a means to greatly expand the applicability of shallow-flow models to real landslide problems involving spreading flow, such as flow slides.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.196
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2008
Admission routes2
Has abstractyes

Explore more

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