Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
Bibliographic record
Abstract
Redbed soft rocks, widely distributed in China, are highly susceptible to weathering, disintegration, and strength reduction under environmental and engineering disturbances, posing critical challenges for slope stability. This study investigates the stability and failure mechanisms of high road-cut slopes in redbed regions under excavation, seismic, and rainfall conditions. Numerical simulations were conducted based on actual engineering sites, using the FLAC3D finite difference model to simulate conditions typical of these sites while incorporating realistic geological features such as weak interlayers and fluid–solid coupling effects. Results reveal that under excavation, the slope exhibits displacement discontinuities and stress concentration near weak interlayers. However, the safety factor of the redbed slope remains at 1.58 at this stage, suggesting that large-scale collapses or landslides are unlikely. Seismic loading amplifies displacements and accelerations, with the maximum deformation reaching a shear displacement of 0.81 m, observed in the upper sections of the redbed slope. Under prolonged rainfall, the slope experiences increased saturation and sliding along interlayer surfaces, driven by reduced shear strength. Combined influences of these factors highlight the vulnerability of redbed slopes to localized failure in weakly weathered zones, necessitating targeted reinforcement strategies. These findings provide a deeper understanding of redbed slope behavior under complex conditions, addressing key challenges in geotechnical and transportation infrastructure engineering.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".