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Record W2139221206 · doi:10.1139/t09-141

Experimental modelling of debris flow behaviour using a geotechnical centrifuge

2010· article· en· W2139221206 on OpenAlexvenueno aff
Elisabeth T. Bowman, Jan Laue, Bernd Imre, Sarah M. Springman

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeGeotechnical engineeringDebris flowDebrisGeologyConsolidation (business)Flow (mathematics)Pore water pressureMechanics

Abstract

fetched live from OpenAlex

Physical modelling of debris flows has been carried out in the geotechnical drum centrifuge at ETH Zürich. A new apparatus to model debris flows in the centrifuge is described. The apparatus permits the final reach of a typical debris flow to be modelled within the centrifuge, with unconsolidated material flowing down a slope to deposit as a fan around the drum. Experiments are described for both fixed base conditions and erodible bases. Tests to examine the verification (modelling) of models show that debris flow behaviour is governed mainly by friction and consolidation processes, although some resolution is required between flow behaviour downslope and flow arrest during runout. The results are compared with bulk parameters determined for field-scale debris flows. It is found that some important flow mechanisms, such as contact-dominated behaviour and high pore pressures, are developed that are closer to those developed at field-scale than tests conducted at 1g. Velocity profiles for erodible beds are compared with a semi-empirical expression derived for experimental debris flows at 1g. Normalized velocity profiles are found to be in agreement; however, absolute velocities differ from those predicted. Scaling, the limitations of the apparatus, and potential for future work are discussed.

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 score0.044
Threshold uncertainty score0.997

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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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

Citations76
Published2010
Admission routes1
Has abstractyes

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