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Record W4392520866 · doi:10.1061/9780784485347.011

A Discrete Element Method-Based Simulation of a Block Toppling Failure on an Inclined Surface

2024· article· en· W4392520866 on OpenAlexaff
Hooman Dabirmanesh, Attila M. Zsáki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsDiscrete element methodBlock (permutation group theory)Surface (topology)Computer scienceGeologyGeotechnical engineeringGeometryMathematicsPhysicsMechanics

Abstract

fetched live from OpenAlex

The particle-based discrete element method is used to simulate a block’s failure on an inclined surface. The particles were generated in polygons and bonded using a parallel bond model to simulate blocks with different width-to-length ratios on an inclined surface. The size of the particles can influence block movement (e.g., toppling or sliding) on the inclined surface. By decreasing the particle size, the interlocking forces between the particles are reduced, and slip occurs between the block and inclined surface. Reducing the particle size, however, increases the analysis time, especially when the number of particles is too high. Introducing smooth joints eliminates the effect of overriding particles (dilation) caused by local particle orientations. As a result, the effect of the two surfaces’ asperities will be removed, especially when larger particles are utilized to reduce the analysis time.

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: Methods · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.514

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)0.0000.000

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 head0.022
GPT teacher head0.332
Teacher spread0.310 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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