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Record W2884788118 · doi:10.1155/2018/8412165

Dynamic Indentation Characteristics for Various Spacings and Indentation Depths: A Study Based on Laboratory and Numerical Tests

2018· article· en· W2884788118 on OpenAlexaff
Jie Liu, Wen Wan, Jun Wang

Bibliographic record

VenueAdvances in Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsGeomechanica (Canada)
FundersHunan Institute of EngineeringState Key Laboratory for GeoMechanics and Deep Underground EngineeringChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsIndentationGroove (engineering)Materials scienceComposite materialDissipationStress (linguistics)Geotechnical engineeringStructural engineeringGeologyMetallurgyEngineering

Abstract

fetched live from OpenAlex

Laboratory and numerical study tests were conducted to investigate the dynamic indentation characteristics for various spacings and indentation depths. First, laboratory tests indicate that the increase in the indentation depth first resulted in enlarged groove volumes, caused by fiercer rock breakages between indentations for a fixed spacing; then, the groove volume slightly increased for further increase in indentation depth, whereas the increase in spacing restrained rock breakages and resulted in shrunken grooves. In addition, the numerical study agreed well with laboratory tests that small chips formed at the shallow part of the rock specimen at the early indentation stage, and then, larger chips formed by the crack propagation at deeper parts of the rock specimens when the indentation depth increased. With further increase in indentation depth, crushed powders instead of chips formed. Moreover, the numerical analysis indicates that crack propagation usually leads to the decrease of the indentation force and the dissipation of the stress concentrations at crack tips, whereas the cessation of crack propagation frequently resulted in the increase of the indentation force and the stress concentrations at crack tip with the increase in indentation depth.

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 score0.353
Threshold uncertainty score0.681

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 head0.004
GPT teacher head0.237
Teacher spread0.234 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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