MétaCan
Menu
Back to cohort
Record W2164998047 · doi:10.1139/t2012-034

Development of progressive failure in sensitive clay slopes

2012· article· en· W2164998047 on OpenAlexaffvenue
Pete Quinn, Mark S. Diederichs, R. Kerry Rowe, D. Jean Hutchinson

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsQueen's UniversityBGC Engineering (Canada)University of Victoria
Fundersnot available
KeywordsLandslideGeotechnical engineeringGeologyErosionSoil mechanicsSlope stabilityCatastrophic failureStress (linguistics)Soil waterGeomorphologyMaterials scienceSoil science

Abstract

fetched live from OpenAlex

The development of progressive failure in sensitive clay slopes is analysed using principles from fracture mechanics, where failure stress depends on a material property (toughness) and length of an existing weakness, unlike traditional soil mechanics where failure stress is generally assumed to be only a function of material strength. An existing weakness, or partially developed failure surface, can propagate due to sudden loads, as might be induced by seasonal erosion cycles, small local landslides at a river bank or earthquake shaking. The paper examines the growth of the failure surface over time due to such loads. The analysis shows that a large landslide in sensitive clay can occur after a number of seasonal erosion cycles with no obvious trigger. It also shows that a larger load due to either a small landslide or earthquake can cause a developing failure surface to propagate freely toward general collapse. Large landslides in sensitive clay are often preceded by a smaller landslide at the river bank, but can be triggered by large earthquakes or may occur for no obvious reason. The agreement between predictions and real behaviour suggests that the model for progressive failure of large landslides in sensitive clay deserves further consideration.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations66
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicLandslides and related hazardsFrench-language works237,207