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Record W2009785436 · doi:10.1139/t10-004

Upper-bound approach for analysis of cantilever retaining walls

2010· article· en· W2009785436 on OpenAlexvenueno aff
Helena Vrecl Kojc, Ludvik Trauner

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsCantileverEmbedmentLateral earth pressureRetaining wallLimit analysisGeotechnical engineeringStructural engineeringLimit (mathematics)Limit state designBearing capacityLimitingEngineeringFinite element methodMechanicsMathematicsPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

The proposed method for the analysis of cantilever retaining walls is based on ultimate limit states, but in contrast to other methods, which are recognized worldwide, also considers the condition of vertical force equilibrium, which includes the wall unit weight and the vertical component of the soil–structure interaction. The two-dimensional analytical model with polygonal soil pressure distribution is based on two new characteristics: the parameter α and the passive pressure coefficient at the embedment depth, K b . The kinematic approach of limit analysis is used to examine the limit equilibrium state of the cantilever retaining wall according to soil properties and other loadings. The failure mechanism, composed of a classical determination of the passive pressure in the embedded part of the wall and a kinematically admissible velocity field at the retained side of the wall, estimates the limiting value of the passive earth pressure at the embedment depth. The advantage of the proposed method is that it enables the design of more slender cantilever retaining walls, at which the comparable level of safety for geotechnical and structural bearing capacity limit states is reached, which is the basic condition for safe design of retaining structures.

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: none
Teacher disagreement score0.562
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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