Micromechanical study of loess permeability and seepage erosion based on microstructure functional basic unit and seepage simulation in pore domain
Bibliographic record
Abstract
The vast majority of geological disasters in loess-covered areas are caused by seepage erosion in loess. Therefore, this study focuses on the microscopic mechanism of loess seepage erosion and constructs a loess microstructure model based on particle “core + coat”. On this basis, the scanning electron microscope (SEM) photos are imported into COMSOL to simulate the micro-scale seepage in the pore domain. Through the actual permeability test, combined with the micro-quantitative information obtained by Image-Pro-Plus and Arcgis, the micro-factors affecting loess permeability are quantitatively analyzed by grey relational analysis. The results show that the dry density affects the porosity of loess and ultimately determines the permeability of loess. Different pore types and proportions lead to different seepage erosion of loess. The erosion process mainly occurs at the junction of pores. The sudden increase of velocity, pressure drop, and maximum shear rate at the throat indicate that this area is the main action area of loess seepage erosion. The research results of this study provide an important theoretical basis for the research and prevention of geological disasters and engineering diseases related to seepage deformation and failure in loess area.
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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) | 0.001 | 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".