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Record W2803143299 · doi:10.1139/cgj-2017-0707

Experimental study of dynamic bending failure of Laurentian granite: loading rate and pre-load effects

2018· article· en· W2803143299 on OpenAlexafffundvenue
Wei Yao, Kaiwen Xia, Ajay Kumar Jha

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsFlexural strengthUltimate tensile strengthSplit-Hopkinson pressure barBendingDynamic load testingDynamic loadingStructural engineeringGeotechnical engineeringThree point flexural testBar (unit)Ultimate loadMaterials scienceComposite materialStrain rateGeologyEngineeringFinite element method

Abstract

fetched live from OpenAlex

In deep underground rock engineering projects, rocks are under static pre-load and they may further experience dynamic load due to earthquakes or production blasts. It is thus desirable to consider dynamic failure of rocks subjected to static pre-load. Besides, bending load is commonly encountered near underground openings. Therefore, this study considers the effect of the pre-load on the dynamic bending strength of Laurentian granite (LG). Using a modified split Hopkinson pressure bar system, the semi-circular bend (SCB) method is applied to carry out the bending tests. Five groups of SCB specimens are tested under different pre-loads and loading rates. The results show that under a given pre-load, the flexural tensile strength of LG increases with the loading rate, and decreases with the static pre-load at a given loading rate. The total flexural tensile strength is roughly independent of the pre-load. An empirical equation is used to represent the effects of the loading rate and the pre-load force on the dynamic flexural tensile strength. Furthermore, the flexural tensile strengths measured from SCB tests have higher values than the tensile strengths measured using the Brazilian disc method for the same rock. A nonlocal failure theory is utilized to quantitatively interpret this discrepancy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.587

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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 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

Citations26
Published2018
Admission routes3
Has abstractyes

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