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Record W2385727061

Application of finite elements with joint net to stability analysis of toppling slope

2011· article· en· W2385727061 on OpenAlexaff
Song Yan-hui, Huang Minqi

Bibliographic record

VenueRock and Soil Mechanics · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil, Finite Element Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsJoint (building)Stability (learning theory)Slope stabilitySlope stability analysisNet (polyhedron)Finite element methodGeologyRock mass classificationGeotechnical engineeringSlope stability probability classificationMathematicsStructural engineeringEngineeringGeometryComputer science
DOInot available

Abstract

fetched live from OpenAlex

The finite elements with joint net is a powerful program which has the function of modeling actual joint net of rock mass slope,it not only permits in-situ statistic parameters of the joints to be input into the model directly,but also gives joints the right to yield according to a certain criterion and thus can better simulate the real rock mass structure of the toppling slope and failure pattern.The stability analysis method of the toppling slope and open questions are discussed;the function and input parameter of the finite elements with joint net are introduced;and its some advantages for stability analysis of toppling slope are explained.A case of toppling slopes from Cihaxia hydropower station at the upper reaches of the Yellow River is calculated using this method to analyze its stability and failure pattern.It is shown that the finite elements with joint net is an effective and matter-of-fact method for the stability analysis of toppling slopes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.242
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2011
Admission routes1
Has abstractyes

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