Full-scale model study on variations of soil stress in geosynthetic-reinforced pile-supported track bed with water level change and cyclic loading
Bibliographic record
Abstract
This study presents a full-scale model investigation on variations of soil stress in a geosynthetic-reinforced pile-supported track bed at various water levels and loading cycles, with four testing procedures: water level rising, cyclic loading at high water level, water level lowering, and cyclic loading at low water level. The soil arching effect was revealed, characterized by higher stress above the pile cap. With the water level rising and loading cycles increasing at high water level, this effect becomes more pronounced, until a peak value of dynamic stress concentration ratio is reached. The stable state of soil arching is obtained earlier near the crown of soil arching, but this arching effect develops more significantly at the foot of soil arching. With the water level lowering and loading at low water level, the soil arching effect remains steady, with slightly changed dynamic stresses in the track bed. The geogrid shows a significant impact on the load transfer mechanism for the quasi-static stress: the quasi-static pile-cap stress presents higher values below the geogrid, whereas the opposite trend is observed for the water-bag (subsoil) area. Nevertheless, this mechanism is not obvious with respect to the dynamic stress, with the values showing no distinct difference above and below the geogrid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".