The interactive role of cyclic vertical and torsional stresses on the instability of low-plasticity soils based on hollow cylinder testing
Bibliographic record
Abstract
Soil instability and potential failure under principal stress rotation require greater attention than ever before due to increased operation of heavier and longer high-speed trains. This study focuses on the interplay between cyclic vertical stress and torsional shear stress on the failure condition of a low-plasticity subgrade soil, facilitated by a hollow cylinder apparatus. Combined vertical and torsional loading significantly influences strain response, with increasing torsional stress leading to higher strain accumulation. Moreover, the data indicate that an increase in torsional shear stress is generally accompanied by a swift rise in the EPWP and a corresponding decrease in the soil stiffness. In view of this, a novel parameter, the overall stiffness degradation index ( δo) that simultaneously captures both the vertical and torsional shear effects under principal stress rotation is proposed as an early indicator of instability. In addition, a normalised torsional stress ratio (NTSR), which is the ratio of the amplitude of torsional shear stress to the confining pressure, is introduced to assess the impact of torsional shear stress. Whereby, higher NTSR values correlate with premature inception of failure. These experimental results provide new insights for a better understanding of soil instability under simulated railway loading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".