Evaluation of a Railway Bridge Using Distributed and Discrete Strain Sensors
Bibliographic record
Abstract
With over 100,000 rail bridges in North America, of which many are over 100 years old, railway corporations are interested in developing ever more effective condition assessment and monitoring strategies for their structures.In general, strain gauges have been a common and reliable sensing tool as they can provide low-noise measurements at high sampling frequencies (greater than 100 Hz).However, fully distributed sensors, in the form of fibre optics strain sensors, have the potential to compliment discrete sensors by providing a more complete understanding of structural behaviour under loading.In this paper, a sensor system including fibre optics sensors on the rail and strain gauges on the rail and bridge members was used to measure the strain experienced during train passage on the Newmarket Bridge, located in North Bay, Canada.The test span is a typical open deck through plate girder (TPG) with a floor system consisting of stringers and floor beams.Four locations on the continuously welded rail, an intermediate floor beam supporting two bays, the bottom lateral bracing system in two bays as well as a 4 m length of rail were instrumented with strain gauges and fibre optic sensors.The research objectives were to (i) understand the load path from wheel to rail to bridge and (ii) quantify the stress states of the instrumented bridge members under service traffic.Conclusions drawn from this research will assist the assessment of railway bridge behaviour and improve future monitoring and reinforcement techniques.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".