A Non-Stationary Power Law Model to Predict the Secondary Creep Rate of Rocks
Bibliographic record
Abstract
Empirical power-law model is usually used to relate the secondary creep rate and deviatoric stress for rocks. However, the effect of confining pressure on the secondary creep rate cannot be appropriately taken into account in the empirical power-law model and also has not yet been fully understood. Therefore, its ability for predicting long-term creep behavior of rocks under different stress state remains uncertain. In this study, the power-law creep model is applied to describe some creep experimental results available in the literature, first on a rock salt and then on a mica-quartz schist under different stress states. The results show that the power law model is able to describe and predict the secondary creep rate of the rock salt with a set of stationary model parameters. For the schist, however, the model parameters show an obvious tendency of confining pressure-dependency. A non-stationary power-law model is then proposed to better represent the secondary creep behavior of various rocks. Its ability of describing and predicting the secondary creep behavior of rocks is verified with the experimental results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".