Railway Track Substructure Evaluation Using Instrumented Wheelset Continuous Measurements
Bibliographic record
Abstract
This paper intends to propose a method for indirect evaluation of railway track substructure using the response measurements of the vehicle.Wheel/rail forces and accelerations are measured by instrumented wheelsets (IWS) and accelerometers, respectively.The IWS system is mounted on a freight railcar and collected data over a short line railroad over Canadian shield with large variation in track stiffness.The relative stiffness of the track could be calculated by the ratio of the magnitude of cyclic load and cyclic displacement.Cycle displacements can be interpreted through the integration of the accelerometer data at the most contributing frequency within both loads and accelerations.An algorithm is proposed to detect the most relevant frequency between load and acceleration and calculate the load/displacement ratio accordingly.To verify the proposed method a numerical simulation is developed in a finite element (FEM) software.Using the validated proposed method, the stiffness variation is estimated on a section consisting of an embankment with lateral supports and the grade crossing.The results confirmed the developed methodology could estimate the stiffness variations and detect track features such as the lateral support of the embankment and the grade crossing as well as soft section that may attribute to some poor subgrade conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".