Health monitoring with optical fiber sensors: from human body to civil structures
Bibliographic record
Abstract
Although structural health monitoring and patient monitoring may benefit from the unique advantages of optical fiber sensors (OFS) such as electromagnetic interferences (EMI) immunity, sensor small size and long term reliability, both applications are facing different realities. This paper presents, with practical examples, several OFS technologies ranging from single-point to distributed sensors used to address the health monitoring challenges in medical and in civil engineering fields. OFS for medical applications are single-point, measuring mainly vital parameters such as pressure or temperature. In the intra-aortic balloon pumping (IABP) therapy, a miniature OFS can monitor in situ aortic blood pressure to trigger catheter balloon inflation/deflation in counter-pulsation with heartbeats. Similar sensors reliably monitor the intracranial pressure (ICP) of critical care patients, even during surgical interventions or examinations under medical resonance imaging (MRI). Temperature OFS are also the ideal monitoring solution for such harsh environments. Most of OFS for structural health monitoring are distributed or have long gage length, although quasi-distributed short gage sensors are also used. Those sensors measure mainly strain/load, temperature, pressure and elongation. <i>SOFO</i> type deformation sensors were used to monitor and secure the Bolshoi Moskvoretskiy Bridge in Moscow. Safety of Plavinu dam built on clay and sand in Latvia was increased by monitoring bitumen joints displacement and temperature changes using <i>SMARTape</i> and <i>Temperature Sensitive Cable </i>read with <i>DiTeSt</i> unit. A similar solution was used for monitoring a pipeline built in an unstable area near Rimini in Italy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".