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Record W2888515425 · doi:10.1139/cgj-2016-0157

Load model for designing flexible steel barriers for debris flow mitigation

2018· article· en· W2888515425 on OpenAlexvenueno aff
Corinna Wendeler, Axel Volkwein, Brian W. McArdell, Perry Bartelt

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersKommission für Technologie und Innovation
KeywordsDebris flowDebrisGeotechnical engineeringFlow (mathematics)GeologyHydrostatic equilibriumMechanics

Abstract

fetched live from OpenAlex

Light-weight flexible steel net barriers catch coarse debris, but let some of the fine material and water pass through the net. They are difficult to design so that they can withstand the impact pressures of both boulder-laden granular and water-saturated debris flows. Using results from laboratory and full-scale field tests, a debris flow load model has been developed for flexible barriers in torrent channels. The model accounts for the forces of initial impact as well as the filling process discretized stepwise over time (barriers in the field and laboratory fill continuously). Laboratory tests with fast debris flow front velocities revealed a run-up behaviour that was not observed in the field (“pile-up”). The load model divides the flow forces into a hydrostatic component and a dynamic part depending on a pressure coefficient, the flow velocity, and the density of the flow. This dynamic part, which is more complex to quantify, accounts for the wide-ranging debris flow characteristics from watery and muddy debris floods to granular friction-dominated mass flows.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.232
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations83
Published2018
Admission routes1
Has abstractyes

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