A novel hyper-spherical ring-augmented method for slope reliability analysis accounting for high-dimensional random fields
Bibliographic record
Abstract
Traditional probabilistic slope stability analysis with random variable model cannot effectively accommodate the inherent soil spatial variability and particularly provides less reliable results. To cope with this, this study presents a novel hyper-spherical ring-augmented method for slope reliability analysis accounting for random fields, where Karhunen–Loève (K–L) expansion is employed for random field discretization. However, high-dimensional issues may emerge when discretizing random fields using K–L expansion, as the number of truncated terms required to achieve comparable accuracy can vary significantly between different autocorrelation functions. In this study, the weighted low-discrepancy simulation (WLDS) is augmented by the hyper-spherical coordinate transformation, allowing it to effectively deal with the curse of dimensionality involved in random fields. Moreover, the judgment-based strength reduction strategy is adopted, which simplifies the process by merely determining whether the slope is stable or unstable without calculating the exact factor of safety. Three illustrative examples including different slopes are analyzed to demonstrate the validity of the proposed method. The results demonstrate that the proposed method can accurately estimate failure probabilities with considerably less computational cost than traditional methods for both low- and high-dimensional random fields. Finally, given a specific target reliability index, the relationship between the total sample size and dimensions is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".