Numerical Evaluation of Slope Stability based on Temporal Variation of Hydraulic Conductivity
Bibliographic record
Abstract
Slope failure is a common phenomenon all over the world on both man-made and natural slopes. Prolonged rainfall is one of the climatic factors which is largely responsible for slope failure. During heavy and prolonged rainfall, a part of the rainwater infiltrates through the soil and seeps into the slope. The infiltrated water lowers the matric suction and increases the porewater pressure. Eventually, the generated porewater pressure decreases the strength of the soil which results in slope failures. To evaluate the effect of rainwater seepage on slope stability, it is necessary to investigate the hydraulic conductivity of the slope soil. The objective of this study is to evaluate the effect of hydraulic conductivity on slope failure mechanisms. A finite element analysis of slope stability was conducted using Geo-Studio software. A numerical model was developed and calibrated with field monitoring data. The field monitoring data included the observation of hydraulic conductivity using a Guelph Permeameter. Afterward, the temporal variation of rainfall and hydraulic conductivity was incorporated into the SEEP/W program and the consequent changes in slope stability were evaluated in SLOPE/W. From the numerical analysis, with the identical strength parameters of the soil, different factors of safety were observed when the slope sections retain different hydraulic properties. Based on the numerical analysis, it was observed that hydraulic conductivity greater than 4×10-6 cm/s leads to slope failure. Periodic monitoring of hydraulic conductivity in the field may provide deep insight into rainfall-induced slope failures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".