Influence of Meso-Structure Parameters on Wave Propagation in Soil-Rock Mixture
Bibliographic record
Abstract
The macro mechanical properties of soil-rock mixture are closely related to the meso-structure features of block stones, namely, content, size, and shape. To promote the engineering application of soil-rock mixture, it is important to explore the meso-structure of the mixture, and evaluate its constitutive properties. The previous studies have shown that the wave propagation in the mixture is highly sensitive to the rock content and compaction. To clarify the meso-structure features of soil-rock mixture, this paper establishes a discrete element model of the mixture based on Particle Flow Code (PFC), investigates the wave propagation features in the model with different meso-structure parameters, and analyzes how the meso-structure parameters affect the wave propagation. The results show that: With the growing rock content, the first wave amplitude increased, while the take-off time shortened; With the growing feature size of block stone, the first wave amplitude gradually decreased, while the take-off time gradually lengthened; The soil-rock mixture containing spherical block stones had the highest first wave amplitude and shortest take-off time, while the mixture containing rectangular block stones had the lowest first wave amplitude and longest take-off time. With the growing rock content, the maximum amplitude, dominant frequency, and spectral area all exhibited an increasing trend; With the growing feature size of block stone, the maximum amplitude, dominant frequency, and spectral area all exhibited a decreasing trend.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".