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Record W2156144086 · doi:10.1139/t08-010

Limit equilibrium slope stability analysis using rigid finite elements

2008· article· en· W2156144086 on OpenAlexaffvenue
Dieter Stolle, Peijun Guo

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSlope stability analysisSafety factorSlip (aerodynamics)Slope stabilityFinite element methodLimit analysisGeologyConstraint (computer-aided design)Factor of safetyLimit (mathematics)Geotechnical engineeringMathematicsStructural engineeringMathematical analysisGeometryEngineering

Abstract

fetched live from OpenAlex

This paper revisits the interslice force assumptions associated with the method-of-slices approach to slope stability analysis. A brief review is presented on analysis procedures for this class of problem and a comparison is made between the factor of safety equations derived by Fellenius and a modified form of Bishop’s equation. A simplified rigid finite element method that takes into account progressive yielding through a sliding law is proposed, eliminating the need to provide constraint equations for the variation of interslice forces required by more advanced procedures, such as that developed by Morgenstern and Price. An example is given to demonstrate the proposed procedure and to investigate the sensitivity of the global and local factors of safety to the interslice and basal shear forces. It is demonstrated that the global factor of safety tends not to be sensitive to interslice shear forces when dealing with circular slip. For the slip circles that were analyzed, the Morgenstern and Price procedure yielded slice forces that were similar to those predicted by the proposed method, which takes into account the deformation and failure characteristics of the material comprising the slope.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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 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

Citations15
Published2008
Admission routes2
Has abstractyes

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