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Record W3022901533 · doi:10.1139/cgj-2019-0082

Tensile response and fracturing process in moderate- and high-plasticity clays

2020· article· en· W3022901533 on OpenAlexvenueno aff
Giulia Forlati, Paul Shepley

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsPlasticityGeotechnical engineeringUltimate tensile strengthSinkholeSoil waterMaterials scienceFlexural strengthFracture mechanicsComposite materialWater contentGeologySoil scienceKarst

Abstract

fetched live from OpenAlex

Sinkholes in clay soils can be considered as the collapse of a soil layer previously bridging a void. Here, flexural deformation in the clay drives the formation of tensile cracks from the lowest surface of the layer and the consequent soil collapse is from crack propagation. Considering a simplified model of the sinkhole geometry, this paper aims to describe the tensile and fracture behaviour of clay soils with different plasticity indices. Speswhite kaolin, London, and Durham clays were tested using direct tensile and bending tests. Moderate- and high-plasticity clays showed a nonlinear fracture response with increasing moisture content, while low-plasticity clays demonstrated a linear response. Bending tests confirmed the importance of the moisture content while the plasticity index confirmed the difference in ductile or fragile collapse for fracture propagation. To assess the results, elasto-plastic fracture mechanics (EPFM) theory was applied to clays with appropriate modifications. The analysis demonstrated that EPFM theory provides a good baseline for predicting tensile fracture behaviour in clay soils, which can be extended in future research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 head0.203
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical Journal→Same topicLandslides and related hazards→French-language works237,207→