Parameter determination for nonlinear stress criteria using a simple regression tool
Bibliographic record
Abstract
In geotechnique, laboratory test results are often employed to estimate the parameter values required for the application of criteria commonly used to define specific stress states such as yielding, failure, and residual strength. Many of these criteria rely on a nonlinear formulation relating the different stress tensor components (or the corresponding invariants). There are many regression techniques that can be selected to obtain corresponding parameters based on experimental results. Some of these can be fairly complex but, considering the intrinsic variability of the available data on geomaterials, relatively simple methods are usually deemed sufficient. In that regard, one should favor methods directly available from common software packages. In this paper, the authors present a simple analysis procedure based on Microsoft Excel ® which can be applied to different two- and three-dimensional stress criteria. As an example, the technique is applied to the failure strength of rock samples using two nonlinear criteria.Key words: rock mechanics, failure criterion, curve fitting, regression analyses, parameter determination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".