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Record W2114024787 · doi:10.1139/cgj-2014-0252

A damage constitutive model for rock mass with persistent joints considering joint shear strength

2015· article· en· W2114024787 on OpenAlexvenueno aff
Hongyan Liu, Xiaoping Yuan

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersOklahoma Agricultural Experiment Station
KeywordsRock mass classificationJoint (building)Constitutive equationMesoscopic physicsAnisotropyGeotechnical engineeringGeological Strength IndexShear (geology)GeologyDirect shear testRock mechanicsShear strength (soil)MechanicsMaterials scienceStructural engineeringComposite materialEngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Microcracks and joints, two types of flaws that appear in a rock mass, affect both the rock mass strength and deformability. A model that can simultaneously reflect the effect of these two types of flaws on the mechanical behavior of a rock mass with persistent joints is not yet available. This study focusses on a microcracked rock mass with persistent joints and establishes a mechanical model, accounting for the anisotropy in the rock mass strength and deformability induced by the existence of the joints. Firstly, the compound damage variable from the coupling macroscopic and mesoscopic flaws is deduced based on the Lemaitre strain equivalence hypothesis. Secondly, the corresponding damage constitutive model for a jointed rock mass is set up. Thirdly, the joint shear failure criterion is incorporated into the constitutive model to extend the model. Finally, the results of the calculation examples show that the existence of the joint will reduce the strength, enlarge the deformability, and lead to anisotropy of the rock mass. A series of calculation examples and comparisons validate that the proposed model is capable of presenting the joint-induced anisotropy in rock mass strength and deformability, determining its possible failure modes, and reasonably simulating its complete stress–strain relationship.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.045
GPT teacher head0.217
Teacher spread0.172 · 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 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

Citations45
Published2015
Admission routes1
Has abstractyes

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