Smart determination of borehole number and locations for stability analysis of multi-layered slopes using multiple point statistics and information entropy
Bibliographic record
Abstract
Subsurface stratigraphy of multi-layered slopes is essential and crucial for slope stability analysis. It is usual practice for engineers to interpret stratigraphic boundaries separating different soil layers using both site investigation data and prior knowledge of local geology, but such practice might encounter significant challenge when the site data are very limited. In addition, uncertainty in stratigraphic boundaries has not been explicitly or quantitatively considered in planning of site investigation (e.g., determination of borehole number and locations). There lacks a quantitative and objective tool to determine the optimal locations and number of boreholes for slope stability analysis while accounting for stratigraphic uncertainty. In this study, a smart sampling strategy based on multiple point statistics and information entropy is proposed for delineation of slope subsurface stratigraphy and planning of geotechnical boreholes. It is a data-driven approach that enables an ensemble of prior knowledge within a training image using multiple point statistics. The proposed method not only provides evolution of the most probable interpolation from sparse measurements and the associated interpolation uncertainties, but also adaptively determines the optimal locations of boreholes. Effectiveness of the proposed method is illustrated and validated through both a simulation example and a real case. It is found that the data-driven framework can automatically identify locations of largest interpolation uncertainty within a multi-layered slope conditional on its outcrops, and that the associated stratigraphic uncertainty gradually reduces as borehole number increases. More importantly, the optimal number and locations of boreholes required for slope stability analysis are adaptively determined by the proposed 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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".