A hybrid cohesive phase-field numerical method for the stability analysis of rock slopes with discontinuities
Bibliographic record
Abstract
The stability of rock slope is predominantly controlled by the fracture behavior of structural discontinuities, such as joints, faults, and bedding planes. Landslides of rock slopes usually involve concurrent tensile and shear fracture evolution within a continuous–discontinuous medium, occurring along the discontinuities and within rock mass, which poses challenges for accurately and efficiently predicting landslides and determining the factor of safety (FS). To address this issue, a hybrid cohesive phase-field numerical method based on the gravity increase method is developed. This approach integrates the cohesive joint model and the unified fracture phase-field method, effectively bridging the scale gap between discontinuity and rock mass. Numerical simulations indicate that the developed hybrid method is validated through physical tests on both discontinuities and rock mass, and achieves computational efficiency comparable to continuum methods. It is worth noting that the critical energy release rate has a more significant effect on the stability of rock slopes than the shear strength. Furthermore, the developed hybrid method accurately captures sliding surfaces and highlights the role of rock bridges in resisting landslides, enabling reliable predictions of FS of rock slopes with persistent discontinuities and non-persistent discontinuities. This study lays a foundation for the hybrid cohesive phase-field numerical method, and provides novel insights into the landslide mechanisms of rock slopes with discontinuities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".