Finite element mesh improvement using an a priori local p-refinement for stress analysis of underground excavations
Bibliographic record
Abstract
As our understanding and modeling capabilities evolve, an ever-increasing complexity of models representing the behaviour of geologic medium are analyzed. One way to evaluate these substantial problems is to optimize the underlying discretization of the governing differential equations by concentrating finite elements where solution accuracy counts the most. This paper develops and evaluates the performance of a priori local p-refinement method for finite element mesh improvement for stress analysis of underground excavations. This type of refinement entails a mesh with higher-order elements near the region of interest and lower-order elements elsewhere. The focus of the paper is the automated insertion of transitional elements at the interface of the two regions. The method relies on transitional finite elements in order to connect a mesh of quadratic interpolation order elements with a mesh of linear interpolation order elements. Four types of transitional elements were considered (4-node and 5-node triangles, 5-node and 7-node quadrilaterals). These were incorporated into a finite element code, and their performance was tested using representative problems such as a pressurized cavity or tunnelling through rock. For these problems the global stiffness matrix size was reduced on average by 85% and by 81% for the models using triangles and quadrilaterals, respectively, as a result, the calculation times were considerably shortened as well. While the average percentage of error with respect to the models without improvement, measured at critical points, was 0.04% and 0.02% in the case of triangular and quadrilateral elements, respectively.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".