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Record W2407431032 · doi:10.1139/cgj-2015-0499

Stress–strain pattern–based criterion to assess cyclic shear resistance of soil from laboratory element tests

2016· article· en· W2407431032 on OpenAlexaffvenue
Dharma Wijewickreme, Achala Nishan Soysa

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeotechnical engineeringStiffnessPore water pressureCyclic stressShear stressShear (geology)Triaxial shear testSimple shearStructural engineeringDirect shear testSoil waterMaterials scienceGeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

The cyclic shear response of soils is commonly examined using undrained (or constant-volume) laboratory element tests conducted using triaxial and direct simple shear (DSS) devices. The cyclic resistance ratio (CRR) from these tests is expressed in terms of the number of cycles of loading to reach unacceptable performance that is defined in terms of the attainment of a certain excess pore-water pressure and (or) strain level. While strain accumulation is generally commensurate with excess pore-water pressure, the definition of unacceptable performance in laboratory tests based purely on cyclic strain criteria is not robust. The shear stiffness is a more fundamental parameter in describing engineering performance than the excess pore-water pressure alone or shear strain alone; so far, no criterion has considered shear stiffness to determine CRR. Data from cyclic DSS tests indicate consistent differences inherent in the patterns between the stress–strain loops at initial and later stages of cyclic loading; instead of relatively “smooth” stress–strain loops in the initial parts of loading, nonsmooth changes in incremental stiffness showing “kinks” are notable in the stress–strain loops at large strains. The point of pattern change in a stress–strain loop provides a meaningful basis to determine the CRR (based on unacceptable performance) in cyclic shear tests.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score0.867

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 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

Citations37
Published2016
Admission routes2
Has abstractyes

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