Effect of progressive wetting on permanent deformation of fouled ballast under cyclic loading
Bibliographic record
Abstract
Climate change in recent decades has increased the frequency and intensity of extreme rainfall events, causing varied moisture contents in the ballasted track, which greatly challenges railway operation and track maintenance. Currently, most research about permanent deformation of fouled ballast are under dry condition or with a moisture content in the individual tested sample, which could not fully represent varied moisture conditions in the field induced by varying rainfall intensities. In this paper, permanent deformation of field-sourced fouled ballast under progressive wetting condition was investigated using large-scale triaxial cyclic tests. The results indicate that, with progressively rising water contents (0% to 12%), the fouled ballast sample maintained their stability; however, the rate of permanent strain increases to a peak value before experiencing a slight decline, aligning with classified shakedown ranges. This observed deformation features can be attributed to both the negative effect of suction loss under progressive wetting and the positive cyclic densification effect of coarse aggregates under repeated loading, an aspect that existing research have not elaborated on. To encapsulate the identified deformation characteristics, this study proposes and validates a new predictive model for the permanent deformation of fouled ballast capturing the feature of progressive wetting.
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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 | 0.000 | 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".