Embankment Displacement PLAXIS Simulation and Microstructural Behavior of Treated-Coal Gangue
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".