Uncertainty assessment of effective friction angle of non-cohesive materials combining data from cone penetration and shear tests
Bibliographic record
Abstract
The effective shear strength is a critical parameter for evaluating ultimate and serviceability limit states of geotechnical structures. To conduct a fully probabilistic assessment or to determine characteristic values according to the second generation of Eurocodes, it is essential to quantify the uncertainty of ground properties due to inherent variability, measurement error, transformation, and statistical uncertainty. However, unlike other ground properties, shear strength parameters are not directly measured, even in laboratory settings. Instead, they are derived from the relationship between shear and normal stresses, making uncertainty analysis nontrivial. This study applies two regression approaches and the extended multivariate approach (EMA) to estimate the effective friction angle for non-cohesive soils. Firstly, an ordinary least squares (OLS) and a Bayesian linear regression (BLR) approach are utilized to quantify the uncertainties inherent in data from direct shear and triaxial tests from an offshore wind project. Secondly, the EMA is utilized to integrate cone penetration tests (CPT) and shear test data via Bayesian inference. The results are discussed based on characteristic values according to Eurocode 7 (EN 1997-1:2024) highlighting the importance of accurately and precisely estimating mean and uncertainty.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".