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Record W2137180758 · doi:10.1139/t06-062

Stochastic stability analysis of a test excavation involving spatially variable subsoil

2006· article· en· W2137180758 on OpenAlexvenueno aff
Yujie Wang, Paul Chiasson

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSpatial variabilityStability (learning theory)Slope stabilityShear strength (soil)Random variableStochastic modellingGeologySlope stability analysisCreepSubsoilPore water pressureMathematicsStatisticsSoil scienceMaterials scienceComputer science

Abstract

fetched live from OpenAlex

A stochastic slope stability analysis method is proposed to investigate the short-term stability of unsupported excavation works in a soft clay deposit having spatially variable properties. Spatial variability of undrained shear strength is modelled by a stochastic model that is the sum of a trend component and a fluctuation component. The undrained shear strength trend, which is also spatially variable, is modelled by kriging or a random function. Slope stability analyses are performed on the stochastic soft clay model to investigate the contribution of spatial variability of undrained shear strength to a disagreement among high factors of safety computed from deterministic methods for slopes that have failed. Probabilities of failure as computed from the stochastic analyses give a better assessment of failure potential. Probability of failure values also correlate with time delay before failure. This phenomenon may be related to progressive failure or creep and to pore pressure dissipation with time.Key words: slope stability analysis, failure probability, spatial variability, stochastic modelling, geostatistics, vane tests, sensitive clay.

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)0.001
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: none
Teacher disagreement score0.888
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.006
GPT teacher head0.170
Teacher spread0.164 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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