Influence of water and rock particle contents on the shear behaviour of a SRM
Bibliographic record
Abstract
Abstract The contents of both water and rock particles are important factors affecting the mechanical strength of a soil–rock mixture (SRM) filled subgrade in the western mountainous area of China. Therefore, the purpose of this paper is to study the mechanisms of reconstituted landslide deposit samples with different water and rock particle contents by analysing the characteristics of shear strength, volumetric strain and ‘jumping’ phenomenon via large-scale direct shear tests. The results show that the influence of water content on shear strength is greater than the influence of rock particle content under a lower normal stress, and the results are reversed in the case of a higher normal stress. The effect of water content on the equivalent cohesion is bigger, especially for the sample with a high rock particle content. The friction angle of the specimen with same water content increases with the increasing rock particle content, but when the number of rock particles increases to a certain extent, there is a little effect on the friction angle. However, the friction angle decreases with increasing water content at the same rock particle content. Specimens with the same rock particle content change from dilation to compression with increasing water content. Finally, the continuous stage of the ‘intense jumping’ at different water content has been analysed. The ‘jumping’ phenomenon of samples with low water and rock particle content will first strengthen and then weaken the samples with increasing normal stress.
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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 | 0.000 |
| 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) | 0.001 | 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".