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Record W2765252388 · doi:10.1139/cgj-2017-0256

Influence of strain energy released from a test machine on rock failure process

2017· article· en· W2765252388 on OpenAlexaffvenue
Yuhang Xu, Ming Cai

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLaurentian University
Fundersnot available
KeywordsStrain energyGeotechnical engineeringEnergy (signal processing)Finite element methodInstabilityDeformation (meteorology)StiffnessRock mechanicsFailure mechanismRock mass classificationProcess (computing)GeologyStructural engineeringEngineeringMechanicsComputer science

Abstract

fetched live from OpenAlex

Rock instability occurs if the energy supplied to the rock failure process is excessive. The theoretical analysis on the energy transfer in the process of rock failure revealed that the rock failure process is a result of the strain energy released from the test machine or the surrounding rock masses of wall rock, plus the additional energy input from an external energy source if the deformation of the rock is continued and driven by the external energy source. The strain energy released from the test machine is the focus in this study because it is responsible for some of the unstable rock failures in laboratory testing. A finite element method (FEM)-based numerical experiment was carried out to study the strain energy released from test machines under different loading conditions of loading system stiffness (LSS). The modeling results demonstrated that depending on the LSS of a test machine, the strain energy released from the test machine alone without additional energy supply can drastically affect the rock failure process. The insight gained from this study can explain unstable rock failure in laboratory tests and the mechanism of some delayed rockbursts that occurred sometime after the excavation of the openings.

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 score0.034
Threshold uncertainty score0.995

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 science0.0010.000
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 head0.007
GPT teacher head0.205
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

Citations33
Published2017
Admission routes2
Has abstractyes

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