Probabilistic analysis of soil nails placed in random soil fields with rotated anisotropic strength
Bibliographic record
Abstract
Soil nails used to support excavations may be inserted into soils with bedding structures at different orientations. This paper examines for the first time the influence of such cases on probabilistic margins of safety for a typical soil nail arrangement by assigning rotated anisotropic random fields of soil strength parameters that are aligned with the bedding structure of the soil domain. The results using rotated anisotropic random fields on statistical outcomes for the computed global factor of safety are compared to results using homogeneous and isotropic random fields, and deterministic analyses. Analyses using rotated anisotropic random fields show that there is a detectable worst bedding direction that gives the highest probability of failure. However, the range of factor of safety for all random field cases is small and remains in the vicinity of the deterministic value. This is ascribed to the ability of the soil nails to anchor the entire reinforced soil domain together and prevent preferential failure paths to develop through the soil. The paper includes the calculation of the confidence in the point estimate of probability of failure for different conditions, and lessons learned in selecting domain size and level of discretization using the random finite difference method.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".