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Record W2320688519 · doi:10.1061/9780784413272.303

Comparison Between 3D Limit Equilibrium and Shear Strength Reduction Methodologies

2014· article· en· W2320688519 on OpenAlexaff
H. H. Lu, Ling-Yu Xu, M. D. Fredlund, D. G. Fredlund

Bibliographic record

VenueGeo-Congress 2014 Technical Papers · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsSoilVision Systems (Canada)
Fundersnot available
KeywordsFinite element methodLimit analysisConservatismLimit (mathematics)Static analysisStrength reductionStructural engineeringShear (geology)Slope stability analysisBenchmark (surveying)Computer scienceEngineeringGeotechnical engineeringMathematicsGeologySlope stabilityMathematical analysisLaw

Abstract

fetched live from OpenAlex

Limit equilibrium analysis of slopes has been commonplace in the geotechnical industry for many years. The 2D approach is conservative in that 3D geometric influences are not accounted for in the 2D analysis. The conservatism associated with a 2D analysis has been viewed as a "buffer" or added factor of safety but such conservatism can be problematic. Recent software tools allow for an improved analysis of 3D slopes through limit equilibrium analysis techniques. The industry as a whole must also examine the use of 3D analysis in light of how design expectations are managed. The shear strength reduction (SSR) technique calculates the factor of safety based on an FEM analysis of stresses. However, the SSR technique remains a new analysis in the geotechnical industry. The purpose of this paper is to compare 3D finite element stability analysis with 3D limit equilibrium analysis through the examination of benchmark examples. The classic differences between 2D finite element stress analysis and 2D limit equilibrium stress analysis will be examined. Continuity when going from 2D to 3D analysis is examined.

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)
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 score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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