Prediction of Internal Erosion Parameters of Clay Soils Using Initial Physical Properties
Bibliographic record
Abstract
This paper aims to predict the internal erosion rate index and critical shear of soils based on the initial physical properties of soils. Regression statistical analyses were employed on sixteen types of clayey soils prepared at different initial dry densities and water contents. The Hole Erosion test was conducted to determine the internal erosion parameters: the erosion rate index and the critical shear. Another set of specimens with the same initial dry unit weight and water content was remolded in the direct shear box and tested using the direct shear test to determine the shear strength parameters (i.e., the cohesion and the angle of internal friction). The various physical properties of soil (initial dry unit weight, initial water content, plastic index, liquid limit, optimum water content, maximum dry density, cohesion, and angle of internal friction) were used to develop models that predict both the erosion rate index and the critical shear. The findings show that the initial physical properties can be used to predict the erosion rate index and the critical shear. The coefficient of determination (R2) was found to be between 0.83 and 0.92 to predict the erosion rate index and between 0.85 and 0.9 to predict the critical shear. The high R2 implies that the models can be used to rate the soil erodibility in advance based on simple laboratory testing instead of time-consuming tests. Additionally, the findings give varied options for prediction depending on the availability of the soil initial physical properties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".