From macro-morphology to micro-mechanics: a deep dive into hydraulic fracturing of compacted bentonite
Bibliographic record
Abstract
A series of injection tests with compacted Gaomiaozi bentonite of varying dry density were performed using an innovative testing apparatus for hydraulic fracturing visualization, with emphases placed on fracturing macro-morphological dynamics and micro-mechanical mechanisms. Results showed that fracturing dynamics mainly consisted of three stages, namely the hydrating stage, the cracking stage, and the fracturing stage. As dry density increased, the circular hydration zone expanded, the cracking network became more intricate, and the fracturing pattern transitioned from long and clear to short and fuzzy. Breakthrough time and breakthrough pressure increased exponentially with increasing dry density, while injection rate decreased exponentially and breakthrough water volume increased linearly. The fracturing mechanism in compacted bentonite was turning from tensile to shear mode with increasing dry density. By comparing the shear strength with the sum of swelling pressure and tensile strength, a simple criterion for determining the limit state between tensile and shear modes was established, which can be popularized in other soils without swelling potential but under different confining pressures. Finally, a novel calculation method for breakthrough pressure was proposed based on cavity expansion theory, taking into account swelling effects and fracturing failure modes.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".