Bibliographic record
Abstract
Structural Health Monitoring (SHM) is a sought after concept that is expected to advance military maintenance programs, increase platform operational safety and reduce its life cycle cost. Such concept is further considered to constitute a major building block of any Integrated Health Management (IHM) capability. Since 65% to 80% of military assets' Life Cycle Cost (LCC) is devoted to operations and support (O&S), the aerospace industry and military sectors continue to look for opportunities to exploit SHM systems, capability and tools. Over the past several years, countless SHM concepts and technologies have emerged. Among those, fiber optic based systems were identified of significant potential. This paper introduces the elements of an SHM system and investigates key issues impeding the commercial implementation of fiber optic based SHM capability. In particular, this paper presents an experimental study of short gauge, intrinsic, spectrometric-based in-fiber Bragg grating sensors, for potential use as a component of an SHM system. Fiber optic Bragg grating sensors are evaluated against resistance strain gauges for strain monitoring, sensitivity, accuracy, reliability, and fatigue durability. Strain field disturbance is also investigated by "embedding" the sensors under a photoelastic coating in order to illustrate sensor intrusiveness in an embedded configuration.
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 | 0.000 | 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) | 0.002 | 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".