RLEM versus RFEM in Stochastic Slope Stability Analyses in Geomechanics
Bibliographic record
Abstract
Spatial variability of geotechnical engineering parameters is an incontrovertible feature, which cannot be overlooked when embarking on stability analyses in soil mechanics. A plethora of methodologies and studies is reported by different researchers across the globe, all bearing witness to the crucial importance of the probabilistic/stochastic variation of soil strength parameters. However, the reliability of different methodologies in substantiation of the inherent variability of natural deposits is not necessarily similar. Chronologically, the Random Finite-Element Method (RFEM) first emerged to contribute to this field. However, with some very promising results, it now transpires that the Random Limit Equilibrium Method (RLEM) is a very robust technique in slope stability analysis, when comparing both the accuracy and time efficiency involved in the calculation process. The current study aims to shed more light on the issue by investigating some comparative stochastic slope stability analyses. Results of some RLEM slope stability analyses are compared with some corresponding RFEM results on a one-on-one basis. The brilliant performance of RLEM in this study obviates the need for cumbersome RFEM calculations, at least in the realm of stochastic slope stability analysis.
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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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".