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Record W1742448746 · doi:10.1139/cgj-2014-0019

Residual shear strength of fine-grained soils and soil–solid interfaces at low effective normal stresses

2014· article· en· W1742448746 on OpenAlexaffvenue
Hisham T. Eid, Ruslan S. Amarasinghe, Khaled H. Rabie, Dharma Wijewickreme

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
FundersQatar National Research Fund
KeywordsGeotechnical engineeringSoil waterShear (geology)Direct shear testShear stressMaterials scienceGeologyComposite materialSoil science

Abstract

fetched live from OpenAlex

A laboratory research program was undertaken to study the large-strain shear strength characteristics of fine-grained soils under low effective normal stresses (∼3–7 kPa). Soils that cover a wide range of plasticity and composition were utilized in the program. The interface shear strength of these soils against a number of solid surfaces having different roughness was also investigated at similar low effective normal stress levels. The findings contribute to advancing the knowledge of the parameters needed for the design of pipelines placed on sea beds and the stability analysis of shallow soil slopes. A Bromhead-type torsional ring-shear apparatus was modified to suit measuring soil–soil and soil–solid interface residual shear strengths at the low effective normal stresses. In consideration of increasing the accuracy of assessment and depicting the full-scale field behavior, the interface residual shear strengths were also measured using a macroscale interface direct shear device with a plan interface shear area of ∼3.0 m 2 . Correlations are developed to estimate the soil–soil and soil–solid interface residual shear strengths at low effective normal stresses. The correlations are compared with soil–soil and soil–solid interface drained residual shear strengths and correlations presented in the literature.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
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 integrity0.0000.001
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 head0.004
GPT teacher head0.186
Teacher spread0.183 · 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 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

Citations69
Published2014
Admission routes2
Has abstractyes

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