Study on compaction characteristics of soft and hard red-bed rock fillers and energy dissipation model of particle crushing
Bibliographic record
Abstract
To explore the compaction characteristics of soft (mudstone) and hard (sandstone) red-bed rock fillers (SHRRF) and the energy dissipation mechanism of particle crushing, this study considered factors such as the soft and hard rock mixed ratio, fractal dimension, maximum particle size, compaction thickness, and compaction work. Through indoor compaction and screening tests, the influence of various factors on the dry density of fillers and the degree of particle crushing was systematically analyzed, and a particle-crushing energy dissipation model was developed, established on the principle of energy conservation and incorporating fracture mechanics concepts to describe the transformation of compaction energy into new surface energy and related energy losses. The research results show that the fractal dimension can define the good gradation of fillers in the range of 1.895-2.635. Importantly, when the maximum particle size does not exceed 40 mm, the upper limit of the fractal dimension should be adjusted to 2.624, which provides a more accurate guideline for gradation control in engineering practice. The increase in hard rock content can increase the maximum dry density of fillers and optimize compaction performance; the increase in fractal dimension helps to optimize the particle gradation of fillers, make the fillers more compact, increase the maximum dry density, and slightly increase the optimal moisture content. Fillers with smaller maximum particle sizes can obtain higher dry density and optimal moisture content under the same compaction conditions, while fillers with larger particle sizes have lower density and are suitable for lower optimal moisture content. Appropriately increasing the number of compaction layers and the number of compaction times per layer can improve the overall density, but the effect weakens after exceeding a certain number of compaction times. The particle crushing rate is inversely proportional to the hard rock content and increases exponentially with the maximum particle size. When the maximum particle size exceeds 40 mm, the crushing rate increases significantly. It is recommended that the maximum particle size be controlled within 40 mm. Energy dissipation analysis shows that the added surface energy increases nonlinearly with the number of compaction times and tends to be stable after 90 times, which is consistent with the trend of particle crushing rate and maximum dry density; the energy utilization rate decays exponentially with the number of compaction times, which is mainly affected by the increased difficulty of particle crushing and the fine particle encapsulation effect. The research results can provide a scientific basis for the rational selection and engineering application of SHRRF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".