Hydro-mechanical coupled behavior analysis of multi-jointed rock mass under triaxial compression based on the discrete element method
Bibliographic record
Abstract
Accurate assessment of jointed rock mass hydro-mechanical (HM) behavior prevents engineering geohazards caused by water pressure. This study conducts triaxial compression tests on jointed rock samples under varying water pressures using the discrete element method. A full-coupled HM model links mechanical and hydraulic fields, with joint fluid flow following the cubic law. On the basis of material parameter calibration against laboratory test data, the study explores the HM coupling behavior of blocky and stochastically jointed rock. An innovative approach integrates ultrasonic pulse velocity (UPV) testing with triaxial compression tests to create stochastically jointed rock models aligned with the Geological Strength Index (GSI) chart. This UPV-based calibration method enables representation of stochastically jointed rock models as blocky/disturbed rock masses within the GSI chart. Simulations identify rotation and detachment failure modes in rock blocks under high water pressure and low confining pressure. Results show that the generalized Hoek–Brown criterion, expressed in terms of effective stress, is unsuitable for rock masses with few joints, particularly under high water pressure. The effective stress principle applies only to highly blocky or less interlocked rock mass structures in the GSI chart. The study highlights joint connectivity's significant influence on the effective stress coefficient.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".