Bibliographic record
Abstract
The increased requirement to operate military platforms and aerospace structures beyond their designed life imposes heavy maintenance and inspection burden on aircraft operators and owners. In-service structural health monitoring is potentially a cost-effective approach by which service usage information can be obtained and knowledgeable decisions can be made. Advanced sensor technology, such as optical fibres, are expected to provide existing and future aircraft with added intelligence and functionality, reduced weight and cost, enhanced robustness and performance. This paper furthers the understanding of technical and practical issues related to full implementation of a fibre optic sensor based structural health monitoring system for aerospace and military platforms. It also reports experimental findings on the use of fibre Bragg grating sensors for measurement of parameters relevant to aircraft structural monitoring and smart structures; with an emphasis on the suitability of multifunctional fibre optic sensor system. Experimental evaluations revealed that Bragg grating sensors correlate well with conventional sensors technology for temperature, stain, crack growth and cure monitoring and were insensitive to pressures up to 300 psi. These sensors were determined to have minimum impact on the structural integrity when embedded parallel to host fibres into composite laminates. Recommendations on the implementation and integration of these sensors into a structural health monitoring system are also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".