Bridge weigh-in-motion using fibre optic sensors
Bibliographic record
Abstract
Bridge weigh-in-motion systems were introduced in the 1970s as a structural health monitoring tool for road bridges. They help bridge operators determine the cause of induced strain in bridges and collect statistics on vehicle weight, class and frequency. There have been over 3000 installations in 25 countries worldwide, which has led to vast improvements in data post-processing. However, existing systems are based on electrical resistance strain gauges, which can be prohibitive in achieving data for long-term monitoring of rural bridges due to power consumption. This paper introduces a new low-power system using fibre optic sensors, which has been pioneered in Northern Ireland. A series of fibre optic sensors were attached to the soffit of an existing integral bridge with a single span of 19 m. The site selection criteria and full installation process are described in this paper. A method of calibration was adopted using live traffic at the bridge site, based on which the accuracy of the system was determined. New methods of axle detection for bridge weigh-in-motion were investigated and verified in the field.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".