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Record W3210330343 · doi:10.1139/cgj-2021-0327

Investigation of grouting diffusion in a single rock fracture considering the influences of obstructions

2021· article· en· W3210330343 on OpenAlexvenueno aff
Wenqi Ding, Dong Zhou, Xiaoqing Chen, Chao Duan, Qingzhao Zhang

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringFracture (geology)GroutDiffusionDiffusion processGeologyRheologyMaterials scienceMechanicsComposite materialComputer sciencePhysics

Abstract

fetched live from OpenAlex

Grouting reinforcement was used to improve rock strength and avoid seepage in rock engineering. A self-developed visualised test platform was developed and the influences of different fracture openness on grouting diffusion modes were revealed; the Bingham rheological model was imported to simulate the grouting diffusion process in a single plate fracture, the spatiotemporal distribution of the velocity field under different obstructions was determined using the finite element method. The results indicate that (i) the grout diffuses faster with the increase of fracture openness, while a stagnation effect of the grouting diffusion velocity behind the obstruction occurs. (ii) Due to obstructions, the grouting diffusion process can be divided into four stages: circular diffusion, flat diffusion, vortex diffusion, and butterfly diffusion. (iii) The grouting diffusion area is divided into a fully reinforced zone and a semi-reinforced zone, and the area of the latter increases with the fracture openness, while being little affected by the size of any obstruction. (iv) Furthermore, some new grouting diffusion laws were revealed considering the asymmetrical arrangement of obstructions. The results presented in this work will be helpful for describing and predicting the grouting process in fracture networks.

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 score0.330
Threshold uncertainty score0.848

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 integrity0.0000.001
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 head0.016
GPT teacher head0.198
Teacher spread0.182 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207