Reappraisal of reliability of a slope through hybridisation of regional and site-specific soil shear strength information
Bibliographic record
Abstract
Uncertainty in soil parameters is usually characterised by probability density functions (PDFs), with the influence on system performance represented through the probability of failure. Difficulties in selecting representative PDFs for a project often arise from scarcity of site-specific information, even with ample previous knowledge and test data of similar soil types in the region. This paper proposes an approach to rationally assimilate regional and site-specific information. A newly-compiled regional database of shear strength information for saprolitic soils in Hong Kong is presented, based on results of multi-stage consolidated-undrained triaxial tests. A hierarchical Bayesian model is fitted to the regional database, followed by a Bayesian updating model that produces posterior predictive distributions of shear strength parameters. The posterior estimates incorporate site-specific features into regional information, leading to profound impacts on the evaluation of failure probability for a slope case. To further illustrate the significance of data hybridisation, four semi-hypothetical scenarios are created using the same slope geometry, by assuming that distributions of shear strength parameters are completely known at the site. With the proposed approach, the estimated failure probability approaches the true value with increasing amount of site-specific data, and is more robust than adopting regional data or site data alone.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".