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Record W2588535072 · doi:10.15273/ijge.2015.03.015

Large Lateral Deformation Characteristics of Simulated Columnar Jointed Rock Mass under Uniaxial Compression Tests

2015· article· en· W2588535072 on OpenAlexvenueno aff
Zhi Song, Weimin Xiao, Huayong Ni, Gang Fan

Bibliographic record

VenueInternational journal of geohazards and environment · 2015
Typearticle
Languageen
FieldEngineering
TopicGeomechanics and Mining Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsRock mass classificationDeformation (meteorology)GeologyMaterials scienceCompression (physics)Geotechnical engineeringUniaxial tensionCompression testComposite materialUltimate tensile strength

Abstract

fetched live from OpenAlex

The columnar jointed rock mass is a common structure in extrusive igneous rocks. Due to the columnar joints network, large lateral deformation may result from slipping along these columnar joints and the corresponding lateral strain ratio may greatly exceed the upper limit of Poisson's ratio. Correct understanding of the large lateral deformation characteristics of columnar jointed rock mass is essential to the design of tunnels and underground caverns where the uniaxial compression condition usually occurs. Therefore, in order to perform uniaxial compression tests, simulated columnar jointed rock mass specimens with different dip angles were prepared using plaster mixtures, and the curve of variation of lateral strain ratio to dip angle was obtained. The shape of the curve resembles as inverted U-typed and the maximum of lateral strain ratio occurs at β = 30°. The mechanism of large lateral deformation is explained in light of the dip angle and failure modes, and an experimental equation is presented to predict the variation of lateral strain ratio to dip angle.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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