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Record W3097728082 · doi:10.1680/jgeot.20.p.046

Effects of geometry and soil properties on type and retrogression of landslides in sensitive clays

2020· article· en· W3097728082 on OpenAlexaff
Chen Wang, Bipul Hawlader, Didier Perret, Kenichi Soga

Bibliographic record

VenueGéotechnique · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsGeological Survey of CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsGeotechnical engineeringLandslideGeologyBrittlenessShear (geology)DebrisDeformation (meteorology)Shear strength (soil)Shear stressSoil waterMaterials scienceComposite materialSoil sciencePetrology

Abstract

fetched live from OpenAlex

Flowslide and spread are two common types of landslides in sensitive clays. Empirical criteria, based on single or multiple soil properties and slope geometry, have been proposed for a rough assessment of potential landslide type and retrogression distance. A large variation has been found in the comparison of retrogression distance between empirical equations and field data. In the present study, flowslides and spreads are simulated using a Eulerian-based large-deformation finite-element (FE) method. In addition to strain-softening, a strain-rate-dependent undrained shear strength model that elevates the strain rate effects on the shear strength of liquefied clay flowing at high speed is used. In flowslides, a higher rate of increase in undrained shear strength with depth reduces the depth of subsequent slides, and thereby the retrogression distance. The maximum retrogression occurs for a uniform shear strength profile. The increase in the ratio of horizontal to vertical stress, resistance to downslope movement of the debris and decrease in soil brittleness and slope steepness change the failure pattern from a flowslide to a spread.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.197
Teacher spread0.191 · 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 designBench or experimental
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

Citations22
Published2020
Admission routes1
Has abstractyes

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