MétaCan
Menu
Back to cohort
Record W4383093220 · doi:10.1139/cgj-2022-0495

Stability of slope corners: a displacement-based FEM study

2023· article· en· W4383093220 on OpenAlexvenueno aff
A. I. Timchenko, Jean‐Louis Briaud

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodGeotechnical engineeringSlope stabilityDisplacement (psychology)GeologyStructural engineeringStability (learning theory)Slope stability analysisEngineeringComputer science

Abstract

fetched live from OpenAlex

The stability of slopes is typically evaluated by two-dimensional plane strain analysis. However, many slopes exhibit three-dimensional (3D) geometries, including slope corners. Several researchers have studied the stability of slope corners but found that the factor of safety (FS) was close to the FS for the plane strain case. The results of a new series of 3D finite element method (FEM) simulating slope corners, including slope angle, plan view angle, radius of curvature, and slope height are presented. They confirm that the FS does not vary much for corners compared to the plane strain case. However, 220 cases using an elastic-perfectly plastic soil model together with the FEM shear strength reduction method show that the displacement field is very different at the corners compared to plane strain with differences reaching over 100%. The displacement ratio between corners and plane strain is presented as a function of the plan view angle of the corner. Often failure is defined at a chosen large displacement, as in the ultimate load of foundation elements. If this concept is carried over to slope stability, it would indicate that the FS of corners should be very different from the plane strain case. A possible explanation is discussed.

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 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: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and AnalysisFrench-language works237,207