Creep failure characteristics and damage creep model of red layer soft rock based on Perzyna viscoplastic theory
Bibliographic record
Abstract
Rock creep is essentially a process of damage accumulation. According to the damage evolution of rock under creep conditions, the TAW2000 triaxial test system is used to carry out triaxial creep test on red layer soft rock taken from Yibo Tunnel in Leshan, Sichuan Province, China, and analyzed the creep deformation rule under different confining pressures. Meanwhile, based on the Nishihara model and Weibull distribution function and Perzyna viscoplastic theory, an improved viscoelastic-plastic creep model which can describe the whole process of rock creep failure was established. The critical point damage variable is defined by dividing the creep stage, so that the acceleration creep start time can be determined more accurately. The results show that: (1) The model curves in this paper fit well with the test data, indicating that Weibull distribution function is feasible to describe rock creep damage, and the accuracy and rationality of the model in this paper are verified. (2) Based on Perzyna viscoplastic theory, a more accurate viscoplastic strain expression was established to describe accelerated creep. (3) By defining the critical point damage variables of different creep stages, the relationship between rock creep deformation and damage can be better reflected, which makes up the shortcoming that Nishihara model cannot describe accelerated creep, and enriches the creep constitutive theory of rock materials.
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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 | 0.001 | 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".