A Stepwise Approach to Verification of the Combined Finite-Discrete Element Method for Modelling Instability Around Tunnels in Brittle Rock
Bibliographic record
Abstract
Numerical modelling of excavations in rock has advanced considerably in recent decades. While continuum numerical models form their basis in methods which can be verified by analytical solutions, discontinuum and hybrid numerical modelling software are challenging to verify. This necessitates the development of processes that can verify individual aspects of complex models. The combined finitediscrete element method (FDEM) allows for the numerical representation of progressive fracture in a simulated elastic material. The FDEM is a powerful tool for modelling instability around tunnels in brittle rock; however, significant verification of the method is required for its use in predictive modelling in critical engineering projects. To verify the FDEM for the purpose of modelling instability around tunnels in brittle rock, a multi-method and multi-scale stepwise verification approach is proposed. Multi-scale verification is required due to the practical limitations of current computational power. Simulation of a laboratory scale tests generally requires a mesh size not significantly larger than the median grain size of the material, limiting the size of elements to a few millimeters for most rock types. In tunnel scale models, a larger element sizes must be used. To relate the input parameters obtained from the laboratory scale calibration to parameters which can be used in tunnel-scale modelling, a gradual upscaling process for Unconfined Compressive Strength (UCS) test simulations is developed. The results of the upscaling process provide guidance for input parameter selection for tunnel-scale models, and insight into scale effects in FDEM models. In multi-method verification, equivalent modelling scenarios are represented using analytical and numerical methods of increasing complexity, to allow individual model behaviours to be progressively verified. A pseudo-discontinuum finite element method (FEM) approach is compared with the FDEM for modelling fracture propagation. Agreement of results is found for simulated non-frictional materials; however, for frictional materials, results agreement is not achieved. Further assessment of tunnel model response to pseudo-discontinuum FEM and FDEM input parameters will lead to improvement of input parameter and result equivalency between methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".