Anisotropic strength in discontinuity layout optimisation for undrained slope stability analysis
Bibliographic record
Abstract
Slope stability analysis in 2D ranges from the classical and conventional limit equilibrium method to the robust and computationally demanding finite element (FE) analysis. Discontinuity layout optimisation (DLO) is an interesting intermediate method that applies an upper bound limit analysis with the assumption of rigid-perfectly plastic soil behaviour. Here, the whole soil mass is discretized using a set of potential slip-lines and optimisation is used to identify the critical mechanism that can be formed from a subset of these lines that dissipates the least energy. This method has only been used for isotropic soil models, except for rare studies that included an anisotropic model. This paper introduces the use of an anisotropic failure criterion in DLO, based on the total stress-based NGI-ADP model. The performance of DLO with this simplified NGI-ADP model is compared with respect to failure mechanism and safety factor determined by corresponding FE analysis. The results show good agreement between the two methods and highlight the use of DLO as a powerful method with straightforward input parameters and low computational time for slope stability assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".