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Record W4316037421 · doi:10.2113/2023/8926187

Failure Mechanisms and Constitutive Model of Composite Rock under Dynamic Loading

2023· article· en· W4316037421 on OpenAlexaff
Jinyu Li, Hongbao Zhao, Dongliang Ji, Huhu Wan, Chen Li

Bibliographic record

VenueLithosphere · 2023
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Ottawa
FundersFundamental Research Funds for the Central Universities
KeywordsComposite numberDissipationSplit-Hopkinson pressure barElastic modulusMaterials scienceJoint (building)Rock mechanicsConstitutive equationStress (linguistics)Composite materialGeotechnical engineeringStructural engineeringGeologyStrain rateFinite element methodEngineering

Abstract

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Abstract In order to investigate the dynamic response characteristics of composite rock with different joint angles, static compression test and dynamic impact test are carried out using WDW-300E servo pressure-testing machine and split Hopkinson pressure bar (SHPB) test system. The dynamic compressive strength, energy dissipation, and failure modes are compared between rock coal (R-C) and coal rock (C-R). Furthermore, a 3D SHPB simulation system is constructed using coupling finite difference method and discrete element method (FDM-DEM) to reproduce the energy evolution and failure modes of composite rock with different joint angles, and the fabric tensor is obtained based on secondary development. Finally, a constitutive model of composite rock is established considering joint angles. The results of experiment, simulation, and theoretical analysis show that for the case of uniaxial compression test, with the increase of joint angle, the peak stress of composite rock shows obvious U-shaped change, and the elastic modulus increases gradually while the peak strain decreases gradually. For the case of dynamic impact test, the peak stress, strain, and energy dissipation values of composite rock decrease first and then increase with the increase of angle, and the elastic modulus of composite rock increases monotonically after a slight fluctuation of 30°. The stress, energy dissipation and elastic modulus of composite rock R-C are larger than that of composite rock C-R. However, the strain value of composite rock R-C is generally less than of composite rock C-R. The failure mode of composite rock with small and large angle is mainly splitting under dynamic impact, and the fracture fragmentation is relatively small with high energy absorption rate, while the fracture fragmentation is mainly shear and splitting mixed failure with low energy absorption rate. The energy accumulation and transformation process inside the composite rock are analyzed by 3D simulation system, and the damage of the composite rock under impact is mainly concentrated on the coal side, and peak values of contact number and contact force with different joint angles are calculated by using fabric tensor. Based on Weibull distribution, a constitutive model of composite rock is constructed considering initial damage and dynamic failure effect, which is in good agreement with experimental and simulation results, verifying the correctness of the constructed model.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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