Influence of drying–wetting cycles on the structure and dynamic characteristics of unsaturated clays: an experimental study
Bibliographic record
Abstract
To study the influence of the drying–wetting cycles on the subgrade filler structure and dynamic characteristics of existing ballasted track subgrade, a series of mercury intrusion tests, scanning electron microscopy tests, and dynamic triaxial tests were carried out considering the drying–wetting cycles. The results indicate that drying–wetting cycles can gradually increase the number of micropores, small pores, mesopores and microcracks in the soil, leading to volume shrinkage and cracking. Under cyclic loading, the accumulated axial strain increases exponentially with the number of drying–wetting cycles. The critical cyclic stress decreases exponentially with the increase of drying–wetting cycles. With the increase of the number of drying–wetting cycles, the resilient of the specimens under cyclic loading continues to weaken. When the number of drying–wetting cycles is greater than 5, the phenomenon of “false increase” of resilient modulus will occur due to the large accumulated axial strain. The drying–wetting cycles has a significant effect on the structure and dynamic characteristics of clay subgrade filler. It is necessary to take a series of measures to ensure the safe and stable operation of existing ballasted tracks in response to the effect of drying–wetting cycles.
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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".