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Record W4220909062 · doi:10.18280/acsm.460104

Limit Analyses of the Active Earth Pressure on Rigid Retaining Walls under Strip Loading on Backfills

2022· article· en· W4220909062 on OpenAlexvenueno aff
N. Tallah, A. Mabrouki, A. Boulaouad, M. Khemissa, Alaoua BOUAICHA

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsLateral earth pressureRetaining wallGeotechnical engineeringStructural engineeringFinite element methodFriction angleMaterials sciencePosition (finance)Foundation (evidence)Limit (mathematics)EngineeringMechanicsGeologyPhysicsMathematics

Abstract

fetched live from OpenAlex

Recent studies of retaining walls include experimental studies, numerical analysis and analytical models. Although active earth pressure against retaining structures has received much attention, the evaluation of active earth pressure of backfill when loaded by a strip foundation, has been slightly studied. This paper studies the effect of a strip load on the active pressure force and the distribution of the horizontal stresses on a rigid wall, using a finite element limit analysis. The strip loading is located at different distances from the vertical face of the wall. The OptumG2 code is used to analyze the effect of width of strip surcharge (soil-wall), interface friction angle and soil internal friction angle. New interesting results are demonstrated and presented here: the dependency of the active earth pressure coefficient on both position and width of the strip load, in one hand, and the effect of the internal friction angle of the soil, the soil-wall interface, and the position of the strip loading on the failure mechanism, in the other hand.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.279
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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