The Numerical Simulation of Hard Rocks for Tunnelling Purposes at Great Depths: A Comparison between the Hybrid FDEM Method and Continuous Techniques
Bibliographic record
Abstract
Tunnelling processes lead to stress changes surrounding an underground opening resulting in the disturbance and potential damage of the surrounding ground. Especially, when it comes to hard rocks at great depths, the rockmass is more likely to respond in a brittle manner during the excavation. Continuum numerical modelling and discontinuum techniques have been employed in order to capture the complex nature of fracture initiation and propagation at low‐confinement conditions surrounding an underground opening. In the present study, the hybrid finite‐discrete element method (FDEM) is used and compared to techniques using the finite element method (FEM), in order to investigate the efficiency of these methods in simulating brittle fracturing. The numerical models are calibrated based on data and observations from the Underground Research Laboratory (URL) Test Tunnel, located in Manitoba, Canada. Following the comparison of these models, additional analyses are performed by integrating discrete fracture network (DFN) geometries in order to examine the effect of the explicit simulation of joints in brittle rockmasses. The results show that in both cases, the FDEM method is more capable of capturing the highly damaged zone (HDZ) and the excavation damaged zone (EDZ) compared to results of continuum numerical techniques in such excavations.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".