Three-dimensional numerical analysis for rock slope stability using shear strength reduction method
Bibliographic record
Abstract
Existing numerical modeling of three-dimensional (3D) slopes is performed mainly by using the shear strength reduction (SSR) technique based on the linear Mohr–Coulomb (MC) criterion, whereas the nonlinear failure criterion for rock slope stability is seldom used in slope modeling. However, it is known that rock mass strength is a nonlinear stress function and that, therefore, the linear MC criterion does not agree with the rock mass failure envelope very well. In this research, a nonlinear SSR technique is proposed that can use the Hoek–Brown (HB) criterion to represent the nonlinear behavior of a rock mass in the FLAC3D program to analyze 3D slope stability. Extensive case studies are carried out to investigate the influence of the convergence criterion and boundary conditions on the 3D slope modeling. Results show that the convergence criterion used in the 3D model plays an important role, not only in terms of calculation of the factor of safety (FOS), but also in terms of the shape of the failure surface. The case studies also demonstrate that the value of the FOS for a given slope will be significantly influenced by the boundary condition when the slope angle is less than 50°.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".