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Record W1951120388 · doi:10.1139/cgj-2012-0457

Base liquefaction: a mechanism for shear-induced failure of loose granular slopes

2014· article· en· W1951120388 on OpenAlexafffundvenue
W. Andy Take, Ryley Beddoe

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsQueen's University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsLiquefactionGeotechnical engineeringCentrifugePore water pressureGeologyLandslideSoil liquefactionFailure mechanismShear (geology)Slope stabilityPileMaterials sciencePetrology

Abstract

fetched live from OpenAlex

For the deviatoric strain-softening associated with static liquefaction to occur in a landslide, the soil must be contractile, be subjected to a monotonic loading trigger, and be sufficiently saturated to permit the generation of excess pore-water pressures upon loading. It is hypothesized in this paper that static liquefaction might preferentially occur in the saturated granular soil located at the base of the landslide in certain circumstances rather than the well-drained inclined portion of the slope. This hypothesis was tested using the technique of geotechnical centrifuge modelling with a loose granular slope, which was brought to failure under a step-wise increase in groundwater flux. The rising pore-water pressures eventually led to a small localized failure at the toe of the slope. This toe failure acted as the monotonic loading trigger to shear the loose contractile saturated sand at the base of the slope and cause liquefaction to occur in the base region. A back-analysis of the landslide indicates that these types of analyses should be viewed with great caution as the progressive nature of the event following triggering will be likely to lead to erroneous back-calculations of mobilized strength.

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 score0.935
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations54
Published2014
Admission routes3
Has abstractyes

Explore more

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