Smart FRP Reinforcements for Long-Term Health Monitoring in Infrastructure
Bibliographic record
Abstract
Smart glass fiber-reinforced polymer (FRP) reinforcements with embedded Fabry–Perot fiber-optic sensors are pultruded and investigated in both laboratory and environmental extreme conditions. Various mechanical (quasi-static and cyclic loads, fatigue loads, long-and short-term creep), thermal (from -40 to +60° C), and severe environmental (alkaline solutions with pH12.8) loads are imposed onto the smart FRP tendons, prior to their application in laboratorydesigned concrete beams. A comprehensive testing program is followed for the beams, including thermal exposure during and after concrete curing phases, static and cyclic failure-induced loadings. In all testing programs, the data obtained from the interferometric Fabry–Perot fiber-optic sensors are compared to those from an extensometer, an electrical resistance strain gage, and an LVDT. The study shows that the response, in terms of internal mechanical strain, against applied mechanical and structural loads up to the failure of the concrete beams can be steadily obtained using embedded smart FRP rebars. These results have higher accuracy when compared with other strain-measuring counterparts.
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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 | 0.000 | 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 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".