Comparison of recoated fiber Bragg grating sensors under tension on a steel coupon
Bibliographic record
Abstract
One of the key elements in a structural health monitoring system is the sensing element and data acquisition system. One type of fiber optic sensor used to measure strain is the fiber Bragg grating. Bragg gratings are fabricated using different methods. One method involves placing a mask pattern over the optical fiber and projecting UV light through it to change the refractive index of the core. However, before the grating is written into the core of the fibre, the outer fibre coatings must be stripped away either mechanically or chemically. Fibre Bragg gratings are then recoated after the grating has been written to maintain the strength and flexibility of the fibre by protecting the exposed glass from damage. Acrylate and polyimide are two types of recoat material typically used on fibre Bragg grating sensors. This work is a controlled comparison of polyimide and acrylate recoated fibres for Bragg grating strain sensors. The comparison was carried out using a tension test coupon with recoated FBG and electrical strain gauges bonded to its surface. The tension test specimen was made of cold rolled steel and was designed according to ASTM A30-97a standard. The dimensions were chosen such that three fibre optic sensors and a strain gauge can be attached on each side. The load was applied in 40 με steps until the strain reached approximately 200 µε. The load was then incrementally decreased back to zero. FBG sensors from 2 manufacturers were compared. For the first manufacturer the Acrylate coated sensors required a gauge factor is 0.75 in order for electrical and FBG strain readings to agree. For Polyimide coated sensors, the appropriate gauge factor was very close to the theoretically predicted value of 0.8. Using these gauge factors, the error between the first manufacturers sensor readings and the strain gauges was well within ±5µε. On the other hand, the second manufacturers sensors did not perform nearly as well. Their readings were substantially lower than the corresponding electrical strain gauges readings and varied from 7% to 13% below expected strain readings. This study demonstrates that bonded FBG sensors can reliably measure strain, but that not all manufacturers are producing recoated FBG sensors to the standard required for strain sensing in civil structures.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".