Modelling the Effects of Soil Properties, Rainfall and Road Geometry to Erosion in Unpaved Roads
Bibliographic record
Abstract
Erosion of soils seriously challenges the sustainability and safety in unpaved roads.It leads to faster deterioration of these roads by formation of rills and gullies in the running surface.Many factors related to the soil properties, rainfall parameters, and road geometry affect erodibility of soils at the surface of unpaved roads.However, little is known about the relationships between those factors of erodibility for a single rainfall event.This paper models the contributions of soil properties, intensity and duration of the rainfall, and road's length and gradient to the quantity of eroded soils from unpaved roads.For a 30-minute duration and two consecutive days; rainfall intensities of 30 mm/hr, 51 mm/hr and 68 mm/hr were used to test the erodibility of soils.The tested bed surfaces were set at slopes of 0% and 6%, in a small (large)-scale testing box of 0.6 m (1.2 m) x 0.3 m x 0.17 m (length x width x height).RapidMiner Studio software was used to predict quantities of eroded soils based on the measured eroded soils under the same influencing factors of erodibility.Six predictive models were developed based on the first-and second-day rainfall events.The predictive models can perform well with the Nash and Sutcliffe's coefficients of efficiency (ME) ranging from 0.62 to 0.74.Also, clay content and mean particle size of the surface soils, rainfall intensity and slope gradient were the most contributing factors to the quantity of eroded soils from unpaved roads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".