Reducing shear strength uncertainties in clays by multivariate correlations
Bibliographic record
Abstract
Quantifications of uncertainties in soil shear strengths, including undrained shear strength of clay, are essential for geotechnical reliability-based design. In particular, how to reduce the uncertainties in undrained shear strengths based on all available information by correlation is a practical research subject, given the considerable cost of a typical site investigation. Although it is simple to reduce the uncertainties by correlation when the information is one dimensional (or univariate), it is quite challenging to reduce the uncertainties by using multivariate information through multiple correlations. This study proposes a systematic way of achieving multivariate correlations on undrained shear strengths. A set of simplified equations are obtained through Bayesian analysis for the purpose of reducing uncertainties: the inputs to the equations are the results of in situ or laboratory tests and the outputs are the updated mean values and coefficients of variation (c.o.v.s) of the undrained shear strengths. Two case studies are used to demonstrate the consistency of the proposed simplified equations. Results show that uncertainties in undrained shear strengths can be effectively reduced by incorporating multivariate information. Given that reliability-based design can justify more economical design with reduced uncertainties, the proposed equations essentially link the value of more and better tests directly to final design savings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".