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Record W2424145808 · doi:10.1139/cgj-2015-0481

Estimating total resisting force in flexible barrier impacted by a granular avalanche using physical and numerical modeling

2016· article· en· W2424145808 on OpenAlexaffvenue
Wesley Ashwood, Oldrich Hungr

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDebris flowLandslideDebrisGeotechnical engineeringFlow (mathematics)MechanicsCurrent (fluid)ImpactGeologyScale (ratio)Granular materialStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Flexible barriers are a promising tool for protection against extremely rapid landslides such as debris flow and debris avalanches. With landslide impacts of any size, the total force induced within the barrier and transferred to the anchorage is a fundamental question to design. Current practice limits the investigation to flow parameters, neglecting the behavior of the structure, which can vary significantly. This paper describes steps taken to quantify the total force induced within a flexible barrier. It describes laboratory experiments of dry granular flow against rigid and flexible barriers with observations of resisting force and other filling processes that provide an understanding of the behavior and possible flow–structure interaction for larger scale rapid landslides. Results from the experiments suggest that for granular flows with high discharge the current practice sufficiently quantifies the total force, and for those with lower discharge, the total force is better characterized by active lateral earth pressure calculations. Test results were also used to validate an adaptation to an existing depth-integrated numerical model for landslide mobility to quantify the total force. This model was then used to estimate the resisting forces induced within a full-scale flexible barrier impacted by a controlled debris flow.

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 categoriesnone
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 score0.222
Threshold uncertainty score0.995

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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
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

Citations138
Published2016
Admission routes2
Has abstractyes

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