NUMERICAL MODELLING METHODS AND APPLICATION IN JOINTED ROCK MASS,PART 2:APPLICATION FOR ENGINEERING PRACTICE
Bibliographic record
Abstract
This paper presents the general outline,approach,and procedure of application for numerical modelling methods in rock engineering practices. It is emphasized for numerical analysis to rely on engineering realization and engineering experience when using numerical method to solve engineering problems. An empirical approach is consequently introduced to reasonably estimating the mechanical properties of rock mass. It is also pointed out that the rock mass strengths are significantly underestimated in hydropower engineering practices in China when comparing to the corresponding rock mass properties from mining projects in Canada. Such underestimation is likely to lead to a misunderstanding of numerical modelling results. Additionally,the stress path analysis based on numerical simulations is recommended for the study on stress-induced rock mass problems whereas modelling the behaviour of geological structures is suggested when carrying out numerical investigations under low in-situ stress conditions. All suggestions for these two scenarios are illustrated with application cases of the Itasca program into numerical study of corresponding engineering concerns.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".