Lab and Field-Based Monitoring of Continuous Welded Rail Using Distributed Fibre Optic Sensors and Buckling Assessment With Physics-Based and Data-Driven Models
Bibliographic record
Abstract
Continuous Welded Rails (CWR) have advantages over traditional jointed track, like improved ride comfort, but increasing demand for railway transportation and climate change have led to the degradation of CWR systems. This degradation can lead to a greater likelihood of rail thermal buckling, which could lead to train derailments. To maintain the required level of operational safety and extend the lifespan of current railway systems, structural health monitoring (SHM) could serve as a useful tool in identifying the early signs of deterioration, hence enabling timely maintenance. This thesis investigates the application of distributed fibre optic sensors (DFOS) for monitoring the lateral response of rails under mechanical loading and thermal loading, for the purpose of rail thermal buckling prevention. A series of lab tests were conducted to evaluate two different DFOS technologies to monitor rail buckling under various axial loading, boundary, and restraint conditions. A method was developed to accurately evaluate the rail axial strain, bi-axial bending curvature, lateral deflection, geometric imperfections, and lateral restraint using the DFOS data acquired at service loads. These parameters were then used to create a finite element model (FEM) for estimating the ultimate buckling response of slender structural members based on measurements taken at service loads. Additionally, a digital twin of the laboratory-tested rail was developed using the statistical FEM approach enabling both the true rail response to be inferred and improved predictions of the nonlinear rail response with confidence intervals. Two field monitoring campaigns were conducted to monitor the long-term thermal response of a tangent and a curved rail in Canada during the critical summer period. The long-term thermal response of the rail was analyzed and assessed using the monitored data. The DFOS measurements were used to establish a data-driven model to predict the rail thermal response and to assess the rail lateral support conditions through FEM updating. At higher temperatures the axial strain and curvature were found to increase nonlinearly, and the lateral support stiffness was found to be lower than expected. The short-term rail dynamic response during the passage of a passenger train was also monitored, and used to evaluate the dynamic buckling response of this section of CWR using FEM.
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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".