Experimental and numerical investigation of the performance of self-sensing concrete sleepers
Bibliographic record
Abstract
Prestressed concrete sleepers with built-in fibre optic–based sensing systems have recently been developed to capture performance data within railway networks and to provide critical decision-support information to route managers and operators. To better understand how self-sensing sleepers can be fully utilized within the rail network, a study of their comprehensive performance under controlled conditions must be undertaken. This article presents the results of the full-scale laboratory testing of a self-sensing sleeper supported on ballast. A primary focus of this study was to investigate whether a self-sensing sleeper could also be used to estimate rail seat load, detect cracking, and identify differential ballast settlement. The ultimate capacity and resilience of the embedded fiber Bragg grating sensing system was tested by applying load up until concrete cracking followed by several cyclic load cycles. Through inference of the load versus strain response, the ability of the self-sensing sleeper to detect damage (concrete cracking and loss of ballast support) was evaluated. The experimental results revealed the effectiveness and robustness of the embedded sensing system to continue to provide reliable dynamic strain measurements well beyond the ultimate loading capacity of the prestressed sleeper. Cracking of the top surface of the sleeper was effectively detected by the fiber Bragg grating strain sensors at the mid-span section. After cracking, subsequent load cycles were carried out. During this period, the bottom fiber Bragg grating measurements captured the effects of differential ballast settlement under the rail seats. A three-dimensional nonlinear finite element model was developed to simulate the experimental test setup and to investigate the relation between fiber Bragg grating sensor measurements and rail track response. The combined experimental and numerical results suggest that a self-sensing sleeper may be deployed on an operational railway to provide reliable and long-term measurements of rail axle load and ballast pressure.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".