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Record W2214154143 · doi:10.1111/str.12169

Strain Rate Effects on Dynamic Fractures in Fine‐grained Granitic Rock

2015· article· en· W2214154143 on OpenAlexafffund
C. Huang, B. Mohanty, Zheming Zhu

Bibliographic record

VenueStrain · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSplit-Hopkinson pressure barFracture (geology)Materials scienceDynamic loadingDebrisStrain rateGeotechnical engineeringInertiaMicrostructureStrain (injury)Composite materialGeology

Abstract

fetched live from OpenAlex

Abstract The effect of dynamic strain rates on failure responses of a fine‐grained granitic rock is studied experimentally and theoretically. Theoretical investigation employs a model incorporating dynamic fracture criterion with damage mechanics theory. Experimental investigation is conducted using split Hopkinson pressure bar device. In order to investigate the effects of microstructure on dynamic fracture failure under different loading rates, fragment debris of each tested specimen is collected and analyzed. It is found through the debris analysis that the granitic rock breaks down into the fragment debris in grain size scales and the effect of strain rates on the formation of fragment debris appears to be related to the microstructure of the rock. It is also found that dynamic inertia induced by the dynamic loading can reduce the effect of friction confinement generated by the contact between the cylindrical specimen and two split Hopkinson pressure bars on the dynamic responses of the specimen. Theoretical evaluations agree with the corresponding experimental observations.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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