Veined Rock Performance under Uniaxial and Triaxial Compression Using Calibrated Finite Element Numerical Models
Bibliographic record
Abstract
Geotechnical rockmass characterization is a key task for design of underground and open pit excavations. Hydrothermal veins influence excavation performance by contributing to stress-driven rockmass failure. This study investigates the effects of vein orientation and thickness on stiffness and peak strength of laboratory scale specimens under uniaxial and triaxial compression using finite element numerical experiments of sulfide veined mafic igneous complex (CMET) rocks from El Teniente mine, Chile. The initial numerical models are calibrated to and validated against physical laboratory test data using a multi-step calibration procedure, first of the unveined Lac du Bonnet granite to define the model configuration, and second of unveined and veined CMET. Once calibrated, the numerical experiment involves varying the vein geometry in the veined CMET models by orientation (5 to 85°) and thickness (1, 4, 8 mm). This approach enables systematic investigation of any vein geometry without limitations of physical specimen availability or complexity of physical materials. This methodology greatly improves the value of physical laboratory test data with a limited scope of vein characteristics by using calibrated numerical models to investigate the effects of any other vein geometry. In this study, vein orientation and thickness were both found to have a significant impact on the specimen stiffness and peak strength.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".