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Record W3010293569 · doi:10.3390/min10030218

Embankment Displacement PLAXIS Simulation and Microstructural Behavior of Treated-Coal Gangue

2020· article· en· W3010293569 on OpenAlexaff
Xiaoyun Yang, Yan Zhang, Zhuhan Li

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsMaterials scienceCoalGeotechnical engineeringSubgradeGangueCompactionDisplacement (psychology)Shear strength (soil)Composite materialGeologyMetallurgySoil waterEngineeringWaste management

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the displacement of coal gangue filling material in road construction and microstructural behavior of treated coal gangue by considering the environmental factors of aqueous solutions with different acidity. The displacement analysis of the coal gangue embankment model has been done by means of the finite element method PLAXIS. Furthermore, using the scanning electron microscope, the energy dispersive system, and the Raman spectrometer to analyze the microscopic mechanism in the view of microstructure, elements, the integrity of carbon structure and the stability of chemical bonds of coal gangue. The results show that the larger displacement of the treated coal gangue subgrade is within 4.0 m below the top of the subgrade, and the maximum displacement value is about 7 mm, which is less than the displacement of untreated coal gangue sample. While the treated A-CG and T-CG grain size, surface area and internal friction angle increase, the unstable carbon structure is destroyed, so its shear strength, compression performance, and consolidation effect are improved. S-CG particles are coated with Na-Si-Al gel, which enhances the density, viscosity and shear strength, thus ensuring the stability of the coal gangue subgrade. The treated coal gangue subgrade slope foot displacement, boundary shear stress and safety factor are all meet the specification requirements.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.426

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 head0.019
GPT teacher head0.243
Teacher spread0.225 · 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 designBench or experimental
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

Citations14
Published2020
Admission routes1
Has abstractyes

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