Probabilistic Analysis of a Slope Using RLEM and Cross-Correlated Conditional Random Field
Bibliographic record
Abstract
Probabilistic analyses of slopes using Random Limit Equilibrium Method (RLEM) have been extensively reported in literature.However, in these types of analyses, the generated random fields are based on assumed values of horizontal and vertical correlation lengths.In practice, horizontal and vertical correlation lengths can be measured using CPT data and the data can be used to condition the generated random fields.Conditioning random fields reduces the level of uncertainty in the analysis and helps the simulations to render more reasonable results.In this study, the stability analysis of a simple slope is used to investigate the influence of conditional and unconditional random fields.To generate spatially variable fields, first, some artificial borehole data are employed to correlate the spatially variable friction angle field.Then, considering some typical values for the variability of the cohesion random field and the possible cross-correlation between the two fields, a couple of scenarios are defined to synthesize the spatially variable realizations of the cohesion field.Then, the results of cross-correlated conditioned and unconditioned random fields are compared.The results show that conditioning random field and considering the cross-correlation between soil input parameters significantly reduce the probability of slope failure.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".