Analytical solution for rainwater infiltration in monolithic soil covers under heavy rainfall and its implications for practice
Bibliographic record
Abstract
Global warming has caused frequent occurrences of extreme rainfall events worldwide. Studying the rainwater infiltration in monolithic soil covers (MSCs) and associated slope stability under heavy rainfall presents practical significance for landfill management. This study established an analytical solution for rainwater infiltration in MSCs during heavy rainfall assuming that the soil hydraulic properties follow exponential forms. The analytical solution was used to calculate the factor of safety (FOS) of MSCs after the verification with numerical simulations. It is found that the FOS for the potential slip surface at the cover bottom remained the lowest during and after heavy rainfall. The percolation and FOS might present pronounced “lag effects”, meaning that the maximum percolation rate and minimum FOS occurred after heavy rainfall. A parametric study was conducted to reveal the influencing factors on the hydraulic response and slope stability of MSCs based on the analytical solution. Relevant results demonstrate that the hydraulic performance and slope stability could be improved by decreasing soil saturated hydraulic conductivity, increasing soil desaturation coefficient, and lowering water level in landfills. The results also reveal the existence of a threshold of soil saturated hydraulic conductivity (1 × 10−8 m/s for this study) for controlling the hydraulic performance and slope stability of MSCs. Furthermore, the analytical solution was applied to determine the rainfall intensity–duration threshold curves of MSCs. The results indicate that the obtained curves of MSCs satisfied exponential forms. This study provides an effective tool and valuable guidance for the design and maintenance of MSCs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".