A continuum mechanics based framework for optimizing boundary and finite element meshes associated with underground excavations—accuracy, efficiency and applications
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A framework was developed to address the automatic optimization of the level of geometric detail required for stress analysis of underground excavations in mining, which was presented in the companion paper. The motivation for optimizing the mesh geometry stems from the over‐discretization of computational domain as the digital mine model is built while our knowledge of some of the input parameters is quite limited. Thus, the accuracy of the solution is not expected to be increased with a finely discretized mesh, only the computation time does. Therefore, it is acceptable if the results obtained from an optimized model have accuracy comparable to the uncertainty in input data (e.g. rock mass properties, geology, etc.). Although the mesh optimization framework automates the geometry optimization and reduces computation time, the accuracy of the solution from the resulting geometry must be evaluated to ensure the quality of the solution at the ‘region of interest’. Both a priori (mesh quality) and a posteriori (solution quality) measures are employed along with recording the mesh optimization time. Finally, the applicability of the mesh optimization framework is demonstrated by analysing a number of mining and civil engineering underground models. Copyright © 2005 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it