A Rigorous Approach to Enhance the Slope Stability of Coal Ash Embankments against Extreme Rainfall Events
Bibliographic record
Abstract
This study investigates the pore-water pressure increase associated with rainfall intensity and duration on a coal ash embankment slope and its effect on the slope stability for safe disposal and storage of coal ash storage facilities (CASFs). Several worldwide incidents during the last decades have revealed that rainfall-induced reductions in matric suction can compromise CASF slope stability, necessitating innovative solutions that address the influence of the coupled interplay of hydromechanical factors. For this reason, the key objective of the study is directed toward evaluating the efficacy of covers with capillary barrier effects (CCBE) to improve the stability of coal ash embankments. A comprehensive analysis, involving SEEP/W and SLOPE/W for uncoupled evaluations and SIGMA/W and SLOPE/W for coupled assessments, was conducted to explore various CCBE configurations. These configurations included fine coal ash (FCA)/coarse coal ash (CCA), FCA/fine recycled asphalt (FRA)/coarse recycled asphalt (CRA), and multilayered systems. The study also examined the influence of density, along with varying rainfall intensities and durations. Numerical modeling results suggest superior performance of three-layer systems, especially the FCA/FRA/CRA configuration, in maintaining matric suction and increasing the slope factor of safety. The system effectively relocated the point of maximum displacement away from the slope toe, suggesting a potential mechanism for enhanced stability and prevention of toe displacement failures. In addition, the study found that the performance of loose coal ash slopes improved with the application of passive reinforcement. The summarized research highlights a sustainable waste-covering-waste approach, introducing controlled nonhomogeneity in slopes to improve their stability against environmental factors.
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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 | 0.001 | 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".