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Record W2752300959

Creep can strengthen clay : a matter of long-term slope stability

2016· article· en· W2752300959 on OpenAlexaboutno aff
Roland Pusch, Sven Knutsson, Xiaodong Liu, Ting Yang

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)CreepStability (learning theory)Geotechnical engineeringGeologyMaterials scienceComputer sciencePhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

The matter of long-term stability of long, natural slopes in illitic clay is of great practical importance in Scandinavia and Canada and has been frequently discussed among geotechnical specialists. A remaining question is how such natural slopes can have remained stable, yet undergoing large strain, for hundreds and thousands of years, during which critical conditions have repeatedly occurred with calculated safety factors lower than or equal to unity according to common stability calculations based on plastic theory. The reason for this may be the role of creep shear strain that causes redistribution of stress and earth pressure leading to a state of equilibrium that is very sensitive to disturbance and represents a condition of near-failure. Triggering of occasional slides can be explained by temporary high porewater pressure caused by periods of intense rain, disturbance by pile driving, or loading by road construction etc, taken place in slopes that have been stable for very long periods of time. The mechanisms by which creep can lead to stable conditions of very old clay slopes can have the form of successive relative particle movements into a state where the interparticle bonds become stronger but of brittle character, according to a model based on stochastical mechanics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.236
Teacher spread0.221 · 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.

Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)Same topicLandslides and related hazardsFrench-language works237,207