Three Dimensional Finite Element Optimization Using the Partial p-Adaptive Method for Stress Analysis of Underground Excavations with Prismatic Cross-sections
Bibliographic record
Abstract
As the complexity and challenges in the field of geomechanics rise, reducing computational costs has become a major theme to be investigated. This thesis evaluates the p-adaptive mesh optimization method’s performance for problems in the 3D finite element stress analysis of underground excavations with prismatic cross-sections. The p-adaptivity changes the element formulation within the finite element mesh, based on the concept of excavation disturbed zone. The changes in element formulation require the use of transition elements to connect linear hexahedral finite elements with quadratic ones. The forthcoming research was conducted using sim|FEM (Zsaki 2010), a research computer code intended for excavation design, which solves 2D plane strain and 3D problems. It was written in C++ and its key feature is its capability to test models with transition elements, which is not found in other analysis software. The research project started with an overview of 3D element formulations, both normal and transitional, which were implemented in the code then simple, yet practical models were tested and the results were analyzed. For some models, the results were compared to commercial software to prove that a correct behavior of the elements tested was obtained. Finally, the p-adaptive method was developed for this class of excavations and it was applied to a linear elastic medium with two circular excavations in a triaxial stress field representing a practical scenario of perhaps a transportation tunnel with a service tunnel running beside it excavated in intact rock. Mesh optimized and non-optimized models were compared and the results showed that optimization results in a reduction of the global stiffness matrix size on average by 82 percent and a reduction of solution times by about 82 percent for optimized models tested using 12-node and 16-node transitional elements, respectively, as compared to non-optimized models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".