A Strain Based Method for Determining the Crack Closure and Initiation Stress in Compression Tests
Bibliographic record
Abstract
The pre-peak loading stages of rock in compression tests are divided into four stages (i.e., crack closure, elastic deformation, stable crack growth and unstable crack growth) by identifying the Crack Closure stress (CC), Crack Initiation stress (CI), and crack damage stress. A new method for determining the CC and CI is presented in this paper and compared with previous methods. The new method is called “Continuous Strain Deviation” (CSD), and it solves two problems associated with other methods: 1) determining the limits for the elastic range in laboratory data, and 2) identifying where crack closure or initiation occurs from the subtle changes in the stress-strain data. Starting from an initial point corresponding to 30% to 40% UCS, the proposed algorithm provides a distinct indicator for CC and CI. The CC and CI for Badaling granite and Äspö diorite are determined with the proposed method, results from which are similar to other methods. Sensitivity analyses of the CSD method show that stable CC and CI values could be estimated using any initial point from 30% to 40% UCS. Comparison studies show that the CSD method predicts a smaller stress range and gives a more distinct indicator for both CC and CI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".