3D printing of rock analogues in sand: a tool for design and repeatable testing of geomechanical and transport properties
Bibliographic record
Abstract
Natural rocks can be heterogeneous due to complex diagenetic processes that affect mineralogy and pore architecture. Correlation of geomechanical and transport properties of rocks in three dimensions can lead to large variances in data when tested experimentally. 3D-printing of rock analogues in sand is a promising alternative for experimental testing that can be used to calibrate variables during geotechnical testing. While 3D-printed sand is a homogeneous material, the parameters for creating grain packing and pore infill can be tuned to mimic specific geomechanical and transport properties. Initially, 3D-printed specimens have a low density due to a loose distribution of grains. Herein, we present our efforts at increasing the density through incorporating a roller in the printing process to compact individual layers. We also propose introduction of a more heterogeneous sand mixture that encompasses a wide range of grain-size distributions. Lastly, a discussion between binder saturation (that infills the pore space) of 3D-printed specimens and the axial strength, dimensional control, and porosity is described within. 3D printing of rock analogues is critical in pursuing rigorous destructive tests required for geotechnical and geological engineering because it can provide repeatable, controlled data on rock properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".