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Nonlinear Slip-Failure Surface and Associated Lateral Earth Pressure

2022· article· en· W4210790627 on OpenAlexaff
Sergio Esteban Rosales Garzón, Adel Hanna

Bibliographic record

VenueInternational Journal of Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsConcordia UniversitySNC-Lavalin (Canada)
Fundersnot available
KeywordsSlip (aerodynamics)Nonlinear systemLateral earth pressureShearing (physics)Slip line fieldGeometryGeotechnical engineeringMechanicsStructural engineeringGeologyMaterials scienceEngineeringMathematicsShear (geology)PhysicsComposite material

Abstract

fetched live from OpenAlex

In optimizing the design of retaining structures and monitoring their health, it is important to determine the actual nonlinear slip-failure surface and the associated nonlinear lateral stress distribution. This paper presents a model developed for the nonlinear geometry of active and passive slip-failure surfaces in cohesionless soils and for determining their three main associated variables; that is, lateral earth pressure distribution, coefficient of lateral earth pressure, and location of the resultant lateral force. The variational limit-equilibrium method, as applied to a normally consolidated dry granular media, and the plane-strain critical-state friction angle at failure are used to develop the model. The model outputs the governing geometry of the slip-failure surface and its associated lateral stress distribution as a unique nonlinear function of the ultimate shearing resistance at failure. Mostly existing studies are complex unilateral approaches of the lateral stress or the slip-failure geometry as separated issues. In contrast, the present paper addresses a simple coupled solution at critical state that uses a disambiguated friction angle and avoids arbitrary input assumptions such as the geometry of the slip-failure surface.

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)0.000
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 score0.068
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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
Published2022
Admission routes1
Has abstractyes

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