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Record W2794450887 · doi:10.1155/2018/6953165

Effects of Particle Size on Fault Gouge Frictional Characteristics and Associated Acoustic Emission

2018· article· en· W2794450887 on OpenAlexaff
Cai‐Ping Lu, Tongbin Zhao, Heng Zhang

Bibliographic record

VenueAdvances in Civil Engineering · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesPriority Academic Program Development of Jiangsu Higher Education InstitutionsGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsSlippingFault gougeSlip (aerodynamics)Acoustic emissionMaterials scienceShear (geology)MechanicsInstabilityParticle sizeDrop (telecommunication)Geotechnical engineeringFault (geology)GeologyComposite materialStructural engineeringPhysicsEngineeringSeismologyThermodynamics

Abstract

fetched live from OpenAlex

Our experimental work was designed to explore the particle size effect of simulated fault gouge on slip characteristics by the conventional double‐direct shear friction configuration combined with acoustic emission (AE). The following conclusions were drawn: (1) smaller particles allow for an initially higher compaction rate at a higher speed and longer duration for force chain formation and destruction. The larger the particle size is, the higher the slipping displacement rate is; (2) the smaller the particle size is, the larger the friction coefficient is, and thus the higher the fault strength is. In addition, the larger the shear velocity is, the higher the fault strength is; (3) the smaller the particle size is, the higher the shear stress drop generated by the stick‐slip is, and the stronger the dynamic slip intensity for a stick‐slip period is; and (4) surface defects of forcing blocks possibly help to embed foregoing “stability” and “stable sliding” into the normal stick‐slip stage. Especially, the “stable sliding” is possibly related to formation of stubborn force chains. These findings may shed some insights into further clarification of slipping characteristics and discrimination of precursory signs of fault dynamic instability with different‐sized gouge particles.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.203
Teacher spread0.198 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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