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Record W4416329989 · doi:10.1139/cgj-2025-0471

Probabilistic analysis of soil nails placed in random soil fields with rotated anisotropic strength

2025· article· en· W4416329989 on OpenAlexafffundvenue
Sutang Wang, Richard J. Bathurst, Reza Jamshidi Chenari

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRandom fieldIsotropyAnisotropyFactor of safetyProbabilistic analysis of algorithmsSoil nailingSafety factorDiscretizationMonte Carlo method

Abstract

fetched live from OpenAlex

Soil nails used to support excavations may be inserted into soils with bedding structures at different orientations. This paper examines for the first time the influence of such cases on probabilistic margins of safety for a typical soil nail arrangement by assigning rotated anisotropic random fields of soil strength parameters that are aligned with the bedding structure of the soil domain. The results using rotated anisotropic random fields on statistical outcomes for the computed global factor of safety are compared to results using homogeneous and isotropic random fields, and deterministic analyses. Analyses using rotated anisotropic random fields show that there is a detectable worst bedding direction that gives the highest probability of failure. However, the range of factor of safety for all random field cases is small and remains in the vicinity of the deterministic value. This is ascribed to the ability of the soil nails to anchor the entire reinforced soil domain together and prevent preferential failure paths to develop through the soil. The paper includes the calculation of the confidence in the point estimate of probability of failure for different conditions, and lessons learned in selecting domain size and level of discretization using the random finite difference method.

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 score0.200
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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