Characteristics and mechanism of time on sand powder 3D printing rock analogue: a new method for fractured rock mechanics
Bibliographic record
Abstract
Abstract Laboratory tests are one of the most fundamental and crucial methods in rock mechanics and engineering research. Natural rock specimens are challenging to acquire, and traditional casting methods involve prolonged curing times and cannot produce rock-like specimens with complex internal fractures. Furthermore, 3D printing technologies such as SLA, SLS, and FDM possess inherent limitations. In this study, high-silica sand was used as the printing material, and sand powder 3D printing technology was harnessed to fabricate rock-like specimens. Uniaxial compression tests were performed on specimens with varying placement times, aimed at investigating the impact of placement time on the mechanical properties of sand 3D-printed rock-like specimens. Acoustic emission technology was used to explore the internal state changes during deformation and failure of specimens with different placement times. The findings indicate that the mechanical properties of sand powder 3DP rock-like specimens exhibited no deterioration over time after approximately 7 days of placement. The internal structure remained unchanged across different placement times. This study's outcomes underscore the superiority of sand powder 3D printing technology within the realm of rock mechanics and establish the groundwork for the accurate and efficient fabrication of rock-like specimens through sand powder 3D printing technology in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".