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Record W2608914317 · doi:10.1139/cgj-2016-0502

Influence of debris flow solid fraction on rigid barrier impact

2017· article· en· W2608914317 on OpenAlexvenueno aff
Dongri Song, Charles Wang Wai Ng, Clarence Edward Choi, Gordon G. D. Zhou, J.S.H. Kwan, R.C.H. Koo

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsCentrifugeImpulse (physics)Bending momentGeotechnical engineeringImpactPore water pressureDebris flowImpact pressurePileMaterials scienceFlow (mathematics)DebrisGeologyStructural engineeringEngineeringClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

The dynamics of debris flows are fundamentally governed by the interaction between the solid and fluid phases. However, current approaches used to estimate impact load treat debris flow as an equivalent fluid without considering solid–fluid interaction separately from other factors. In this study, a series of centrifuge tests was carried out to investigate the influence of interaction between solid and fluid phases on single-surge debris flow impact on a rigid barrier. The effect of solid–fluid interaction was studied by varying the solid fraction of the flows. A model rigid barrier was instrumented to capture induced bending moment and impact pressure. Test results demonstrate that the transition from a pile-up mechanism to a run-up mechanism is governed by the solid fraction and thus the grain contact stresses. The rigid barrier design for the impact with a pile-up mechanism is mainly dominated by the static load. Contrary to the hydrodynamic approach, which assumes that the frontal impact is the most critical, the frontal impact of a run-up mechanism contributes less than 25% of the total force impulse. The consideration of static loading leads to the development of a new impact model with a triangular distribution of the impact pressure.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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 designObservational
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

Citations141
Published2017
Admission routes1
Has abstractyes

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