An experimental study on the effect of length and orientation of embedded FBG sensors on the signal properties under fatigue loading
Bibliographic record
Abstract
Abstract Fiber Bragg grating (FBG) sensors provide excellent capability for the structural health monitoring (SHM) of load-bearing structures by allowing for local internal strain measurements within structures. However, the integration of these sensors to composite materials is associated with several challenges that have to be addressed to have the correct strain measurement and in turn to perform reliable SHM. One of the most important issues is the presence of uneven strain fields around FBGs, which significantly affect the response of the sensors and hence the reliability of the acquired data. The uniformity of the strain fields around sensors is important for dependable data acquisition; however, to generate such a condition, tow width-to-FBG length relationship, optical fiber configuration with respect to reinforcement fiber orientation, and crack density resulting from fatigue loading are very important factors that have to be considered. In this paper, these issues are addressed by investigating the signal properties of FBG sensors with 1 and 10 mm lengths embedded within the composite specimens during the manufacturing process. After fatigue testing of the specimens, it is shown that 1-mm-long FBGs embedded in-line with adjacent reinforcement fibers with tow widths of ∼2 mm provide much more reliable signals than 10-mm-long FBGs embedded perpendicular to adjacent tows.
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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 | 0.000 | 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".