Considering multiple failure modes: A comparison of probabilistic analysis and multi-modal optimization for a 3D slope stability case study
Bibliographic record
Abstract
Limit equilibrium (LE) slope stability analysis methods are typically combined with global search methods to locate a single critical slip surface, which corresponds to the minimum factor of safety for a topographical model. However, in complicated 3D models there often exist multiple modes of failure with similar factors of safety within the topography. In such cases, it is necessary to consider multiple slip surfaces in the design process rather than just a single surface. To achieve such a goal, this paper proposes two different methods: 1) probabilistic analysis with stochastic response surfaces (SRS), and 2) the Locally Informed Particle Swarm with Radius Filter (LIPS-R) niching algorithm. SRS is a very fast and effective alternative to Monte Carlo or Latin Hypercube sampling of rock and soil material parameters for probabilistic analysis. LIPS-R is a multi-modal optimization (MMO) algorithm based on a niching method called locally informed particle swarm (LIPS). The purpose of MMO algorithms is to output multiple local minima solutions for a given optimization problem. A 3D case study is presented in this paper where both methods are demonstrated to identify multiple failure modes for an open pit mine. Despite the differences between the proposed methodologies, the failure modes identified in both cases are shown to agree.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".