Structural Health Monitoring using Apodized Pi-Phase Shifted FBG: Decoupling Strain and Temperature Effects
Bibliographic record
Abstract
Fiber Bragg Gratings (FBGs) as tools for structural health monitoring are broadly used for assessing the composite structures behavior under different scenarios to secure the system in a reliable and accurate way. Due to cross-sensitivity effects in optical sensors, one way to enhance the accuracy is to disentangle the strain from other affecting parameters such as temperature or vibration for recognizing the real strain that is experienced by the composite structure. In this work, design and numerical simulations of Apodized Pi-Phase Shifted FBG (π-PSFBG) are presented to evaluate the performance of non-uniform FBG for simultaneous strain and temperature monitoring. Due to specific accuracy and spectral characteristics of the π-PSFBG, it is selected as an optical sensor to enhance the sensitivity of the measurements. Sensor signals are designed and simulated by solving coupled mode equations using the transfer matrix to represent the reflection spectrum of π-PSFBG. To accomplish spectral improvement, the Gaussian apodization function is applied to the FBG reflected spectrum in order to optimize its spectra by suppressing side lobes. Moreover, we have developed π-PSFBG sensor using Neural Networks (NNs) approach to sense and discriminate strain from other affecting gauges such as temperature. The proposed neural networks is trained to learn the relationship between the reflection spectrum and the external parameters such as strain and temperature. Our investigations not only characterize the performance of an apodized π-PSFBG to simultaneously measure two parameters with high sensitivity, but also yield the minimum error in compensation of strain from temperature.
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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 | 0.000 |
| 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) | 0.000 | 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 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".