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Record W4414703193 · doi:10.1002/nag.70087

Importance of Spatial Variability in Probabilistic Stability Analysis of Relatively Steep Undrained Slopes with a Foundation Layer

2025· article· en· W4414703193 on OpenAlexaff
D. V. Griffiths, Desheng Zhu, Gordon A. Fenton

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
FundersUS-UK Fulbright Commission
KeywordsFoundation (evidence)Slope stabilitySpatial variabilityContext (archaeology)Stability (learning theory)Spatial correlationProbabilistic logicSlope failure

Abstract

fetched live from OpenAlex

ABSTRACT The paper investigates the influence of a foundation layer on relatively steep undrained slopes with spatially variable soil strength. The definition of a relatively steep slope in this context is a slope angle that is steeper than that established by Taylor for the transition point between toe failures and base failures in uniform slopes which occurs at around 53°. It is shown that when the soil strength is spatially variable, critical failure mechanisms can pass into the foundation layer even in relatively steep slopes, which could never happen in a uniform soil. Although the worst‐case correlation length is a well‐established phenomenon in geotechnical reliability, it has usually been associated with slopes with relatively low factors of safety based on the mean. The paper demonstrates for the first time that even slopes with high factors of safety based on mean strength, can exhibit a striking worst‐case correlation length, confirming that failure to account for spatial variability can lead to unsafe predictions of the probability of failure.

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)0.002
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.342
Teacher spread0.319 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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