Limit Equilibrium Probabilistic Analysis of Three-Dimensional Open Pit Using the Stochastic Response Surface Method
Bibliographic record
Abstract
Three-dimensional (3D) probabilistic slope stability analysis using limit equilibrium methods is a time-consuming procedure. Using traditional sampling methods such as Monte Carlo or Latin hypercube may take days of computation, especially when there are multiple random variables and complicated geometries involved. Stochastic response surface (SRS) method is a very fast and effective approach for probabilistic analysis of 3D complicated geometries, which reduces the number of simulations and simulation time dramatically. The SRS method uses a small number of samples that cover the parameter space to train the model. Any number of samples can then be plugged into this model and will result in the estimated factor of safety values for each sample. In this study, an SRS algorithm using third-order Hermite polynomial expansion that works effectively for complex 3D probabilistic analysis is presented to show the performance of this method in the probabilistic analysis. A complex open pit model with several random variables has been investigated using the SRS method, and the results are compared with Latin hypercube simulation results. The results using both methods are in good agreement. However, the SRS method computation time was about 7% of that of the Latin hypercube computation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".