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Record W2999963657 · doi:10.1061/9780784482124.020

Probabilistic Analysis of a MSE Wall Considering Spatial Variability of Soil Properties

2019· article· en· W2999963657 on OpenAlexaff
Sina Javankhoshdel, Brigid Cami, Thamer Yacoub, Richard J. Bathurst

Bibliographic record

VenueGeo-Congress 2019 · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsRoyal Military College of CanadaRocscience (Canada)
Fundersnot available
KeywordsCohesion (chemistry)Friction angleProbabilistic logicGeotechnical engineeringFactor of safetyProbabilistic analysis of algorithmsMathematicsSpatial variabilityStructural engineeringEnvironmental scienceGeologyStatisticsEngineeringPhysics

Abstract

fetched live from OpenAlex

The results of probabilistic analyses of a mechanically stabilized earth (MSE) wall with geosynthetic reinforcement are presented. The analyses consider spatial variability of reinforced and foundation soil properties using the 2D non-circular random limit equilibrium method (RLEM). In this study, it is assumed that the reinforced soil is a purely frictional soil, while the foundation is a cohesive-frictional (c-ϕ) soil. A negative cross-correlation between cohesion and friction angle and a positive cross-correlation between cohesion and unit weight, and between friction angle and unit weight are also assumed. It is shown in this study that, considering only random variability of soil properties results in an overly-conservative probability of failure for design. However, considering spatial variability of soil properties plus cross-correlation between soil input parameters provides an estimate of probability of failure which is in better agreement with the margin of safety implied using a deterministic factor of safety.

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 score0.006
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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
Published2019
Admission routes1
Has abstractyes

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