Comparison study of parameter estimation techniques for rock failure criterion models
Bibliographic record
Abstract
This paper presents a comparative study of parameter estimation of rock failure criteria using different statistical methods, such as the least squares, least median squares, and reweighted least squares. The Mises–Schleicher and Drucker–Prager unified (MSDPu) failure criterion, a nonlinear polyaxial failure criterion suitable for different rock strength data, has been considered for this study. The procedure for determining model parameters from scattered data using the least median squares method is presented, and the methods of identifying the scattered data are discussed. The use of the reweighted least squares method for improving statistical performance of model parameter estimation from scattered data is presented. Using the variable transformation technique, the parameter estimation problem has been formulated as an unconstrained minimization problem. Here, both traditional nonlinear programming techniques and recently developed evolutionary optimization algorithms have been employed, and a comparative study of their relative efficacy is presented. Of several traditional nonlinear unconstrained minimization techniques, only the Hooke–Jeeves method could be used to find the optimum value, but its robustness is much lower than that of evolutionary algorithms. Evolutionary algorithms can be applied to find the optimal solution for cases with a high degree of robustness.Key words: rock mechanics, failure criterion, parameter determination, least median squares, evolutionary optimization.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".