Optimization of structural contact stiffness and strength for discrete simulation of progressive failure of healed structure
Bibliographic record
Abstract
Abstract Geotechnical analysis for underground excavation design in complex tectonic environments requires an increased understanding and more rigorous consideration of the impact of healed or "intrablock" structure, such as veins, on rockmass behaviour. Intrablock structure occurs between blocks of rock defined and bounded by "interblock structure", the network of joints and other fractures conventionally considered in classic rockmass characterization, classification or rockmass property estimation. Discrete simulation of fractures has become a more commonplace model analysis technique for excavations in jointed rockmasses. Here too, however, special attention is required to simulate intrablock structure within the model. In particular, the selection and evolution of stiffness and strength values for the model discontinuity elements must follow a different logic than that adopted for fractures and true joints. A new concept to better represent the behaviour of intrablock structure in explicit numerical models is proposed and tested in this paper by means of finite element method (FEM) analysis and case study data from a 1, 200 m deep drift. This approach changes the stiffness and strength values of failed intrablock structural elements between pre‐peak ("primary"), post‐peak ("secondary"), and ultimate ("tertiary") states. The FEM models in the tertiary state match 96 % of overbreak patterns along the case drift, versus 80 % in primary state models. These findings suggest that the proposed method is a good option to more accurately model the influence of intrablock structure on rockmass behaviour.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".