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Record W2771025473 · doi:10.1063/1.4995609

A dynamic point-load test for quantifying rock dynamic strength parameters

2017· article· en· W2771025473 on OpenAlexafffund
Changyi Yu, Wei Yao, Ying Xu, Kaiwen Xia

Bibliographic record

VenueReview of Scientific Instruments · 2017
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsLoad testingPoint (geometry)Dynamic testingMaterials scienceComputer scienceMechanicsGeotechnical engineeringGeologyPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

The point-load test (PLT) has been widely used in the field and in the laboratory to estimate the strength of rock materials. The PLT is easy and quick to perform and it is suitable for samples with irregular shapes and therefore has found wide applications. The measured point-load strength (PLS) is considered as a strength index and it has been correlated to the rock compressive strength. To address the engineering applications where the loading is dynamic, the PLT is extended to its dynamic version in this study. The dynamic loading is exerted to the rock specimen using a split Hopkinson pressure bar system. Two conical steel platens are attached to the incident bar and transmitted bar, respectively, to apply the point load to the disc specimen. To enable quasi-static analysis, the pulse shaper technique is utilized to achieve the dynamic force balance. The flexibility of the dynamic PLT method is demonstrated by the application to a well-studied granitic rock-Laurentian granite. The correlation between the dynamic PLS and the dynamic strength of the same rock is established.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.294
Teacher spread0.261 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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