Geotechnical rheological modeling of ballasted railway tracks considering the effect of principal stress rotation
Bibliographic record
Abstract
The rotation of principal stress direction experienced by the soil elements in a railway track substructure during a train passage influences the magnitude of accumulated settlement. However, the existing methods to evaluate the track response under repeated train loads disregard the influence of principal stress rotation (PSR). This article presents a novel approach for assessing the behavior of ballasted railway tracks incorporating the contribution of PSR on track deformation. The proposed technique employs a geotechnical rheological model to evaluate the track behavior, in which the material plasticity is captured through plastic slider elements. The influence of PSR is accounted for by extending an existing constitutive relationship for the slider elements for the substructure layers, which is successfully validated against experimental data reported in the literature. The results reveal that PSR causes significant cumulative deformation in the substructure layers, and disregarding it in the analysis leads to inaccurate predictions. The proposed approach is then applied to an open track-bridge transition with heterogeneous support conditions, in which the differential settlement is found to be largely influenced by PSR. The findings from this study highlight the importance of including the effect of PSR in predictive models for a reliable evaluation of track performance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".