Radial drainage and consolidation of soft soil subjected to intermittent cyclic loading
Bibliographic record
Abstract
The rail network invariably encounters soft subgrades consisting of shallow estuarine clayey deposits. Cyclic loading generated by the passage of trains causes deformation and corresponding development of excess pore water pressure (EPWP), which dissipates during the rest periods between two consecutive trains. This paper presents an experimental study describing the effect of yield stress and EPWP responses upon intermittent cyclic loading (i.e. with rest periods), and the associated consolidation with the combination of vertical and radial drainage by way of a prefabricated vertical drain (PVD). Based on the laboratory data, the normalised yield stress for cyclic loading (NYCL) is introduced as an insightful parameter to define a novel empirical relationship between the yield stress, cyclic stress amplitude and the initial effective stress. The experimental results indicate that, as the NYCL increases, the peak EPWP decreases and, during the rest periods, the EPWP reaches a stable equilibrium faster without causing further settlement. Furthermore, this study demonstrates that the accumulated EPWP caused by cyclic loading can be further reduced when using a larger width of PVD for a given unit cell radius. An analytical model inspired by empirical parameters for predicting EPWP is proposed, capturing the effects of NYCL and the PVD characteristics.
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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".