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Record W2886697346 · doi:10.1139/cgj-2018-0178

Constitutive model to simulate full deformation and failure process for rocks considering initial compression and residual strength behaviors

2018· article· en· W2886697346 on OpenAlexvenueno aff
Wengui Cao, Xin Tan, Chao Zhang, He Min

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutive equationGeotechnical engineeringResidual strengthDeformation (meteorology)ResidualVoid (composites)Compression (physics)Hardening (computing)SofteningStiffnessMaterials scienceGeologyStructural engineeringFinite element methodComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

A constitutive model with capacity to simulate the full deformation and failure process for rocks considering initial compression and residual strength behaviors is discussed in this paper. The rock was assumed to consist of the initial voids portion and the solid skeleton portion. The full deformation model of rocks can be established by the consideration of the macroscopic deformation of rocks and the microscopic deformations of the two different portions based on the statistical damage theory. Comparisons between the experimental data from triaxial compression tests and calculated results show that the proposed constitutive model provided a good prediction of the full deformation and failure process, including the effects of initial void compression, stiffness degradation, strain hardening–softening, and residual strength.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.542

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)0.0000.000

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 head0.024
GPT teacher head0.272
Teacher spread0.247 · 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

Citations70
Published2018
Admission routes1
Has abstractyes

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