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Record W2090394309 · doi:10.1088/0957-0233/25/7/075002

An experimental method to quantify the impact fatigue behavior of rocks

2014· article· en· W2090394309 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMeasurement Science and Technology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBrittlenessSplit-Hopkinson pressure barMaterials scienceFatigue testingIsotropyDisplacement (psychology)Rock blastingFailure mode and effects analysisStructural engineeringBar (unit)Geotechnical engineeringComposite materialGeologyEngineeringStrain rate

Abstract

fetched live from OpenAlex

Fatigue failure is an important failure mode of engineering materials. The fatigue behavior of both ductile and brittle materials has been under investigation for many years. While the fatigue failure of ductile materials is well established, only a few studies have been carried out on brittle materials. In addition, most fatigue studies on rocks are conducted under quasi-static loading conditions. To address engineering applications involving repeated blasting, this paper proposes a method to quantify the impact fatigue properties of rocks. In this method, a split Hopkinson pressure bar system is adopted to exert impact load on the sample, which is placed in a specially designed steel sleeve to limit the displacement of the sample and thus to enable the recovery of the rock after each impact. The method is then applied to Laurentian granite, which is fine-grained and isotropic material. The results demonstrate that this is a practicable means to conduct impact fatigue tests on rocks and other brittle solids.

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.

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)0.005
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.389
Teacher spread0.316 · 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