Evaluating the Effectiveness of Asphalt Layer in Improving Railway Track Stiffness through 3D Numerical Simulations
Bibliographic record
Abstract
In the last three decades, railways have gradually increased the weight and speed of trains to improve their capacity, profitability, and efficiency. This approach has provided overall benefits; however, it has also led to increased track and structure maintenance costs. The subgrades of railway tracks now experience higher stress, resulting in significant deformation and, in extreme cases, embankment shear failure. Therefore, ground improvement methods should be introduced to reduce stress and deformation in tracks and ensure that the subgrade can safely withstand the increased axle load. One effective ground improvement method is the use of an asphalt layer, which has been successfully applied in many countries. The thickness of the asphalt layer varies from 10 to 20 cm, depending on the regulations of each country. In this study, the finite element program ABAQUS is utilized to model a three-dimensional railway track and investigate the effectiveness of using an asphalt layer to improve the track modulus. The model is calibrated based on experimental observations and used to determine the effects of different combinations of asphalt and granular layers on the stress and displacement of the subgrade under static load. Considering the importance of track modulus in long-term track behavior, the Winkler theory is employed to estimate the track modulus. The results suggest that increasing the thickness of the asphalt layer from 10 to 18 cm significantly reduces the stress and displacement of the subgrade, resulting in uniform displacement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".