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Record W2102055285 · doi:10.1139/cgj-2012-0001

New strength formulae for rock surrounding a circular opening

2013· article· en· W2102055285 on OpenAlexvenueno aff
Wen Fan, Mao-Hong Yu, Longsheng Deng, Liwei Chen

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDilatantSofteningGeotechnical engineeringResidual strengthStrength of materialsPlasticityAiry functionDisplacement (psychology)Shear strength (soil)MechanicsGeologyStructural engineeringMathematicsMaterials scienceEngineeringMathematical analysisPhysicsComposite material

Abstract

fetched live from OpenAlex

In this paper, the unified strength theory proposed by Yu Mao-hong in 1991 is used to develop new strength formulae for rock surrounding a tunnel. The new formulae can be applied to an elastic–plastic material. The formulae have a series of expressions with the unified parameter, especially variations such as the well-known Kastner and Airy formulae, which are widely used in rock mechanics and engineering. These formulae are derived on the basis of the Mohr–Coulomb strength criterion. For the strength-weakening (plastic-softening) of geomaterials, the analytical solutions for the radius of the plastic residual zones, radius of the plastic-softening zones, and displacement around the opening are presented according to the elastoplastic-softening – residual plastic, tri-linear stress–strain model. In addition, the derivation for the stress state in the surrounding rock is given. The Kastner formula, Airy formula, and available solutions can be considered as special cases in the new strength formula. The influence of softening, shear dilatancy, and different strength models on the results is analyzed. The results presented in this paper are useful for analyzing surrounding rock with various stress conditions and reinforcement of caves.

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: none
Teacher disagreement score0.963
Threshold uncertainty score0.869

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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