Laboratory investigation on Fabry-Perot sensor and conventional extensometers for strain measurement in high performance concrete
Bibliographic record
Abstract
Advances in fiber optic sensing technology have made possible the installation of an extremely precise and reliable sensor in small structural members. Because of the high sensitivity and fast response of the sensor, low strain and dynamic strain can be measured. In this study, a Fabry-Perot strain sensor was cast in a high performance concrete cylinder, which had been submitted to simple compression and thermal tests. These results were compared with measurements obtained using external linear variable differential transformers fixed on concrete samples having the same composition as the fiber optic instrumented concrete cylinder. Comparisons were also done with results from tests on concrete cylinders instrumented with embedment vibrating wire and electrical strain gauges. In addition, thermal tests were performed on the different concrete cylinders and samples in order to compare the behaviour of the different sensors in high performance concrete submitted to temperature variations. The results show that the concrete strains measured with the Fabry-Perot sensor are in agreement with strain measurements made on concrete samples. Consequently, the presence of the embedded fiber optic sensor does not influence greatly the mechanical properties of concrete. Furthermore, for high stress levels (0.4 f 'c) and rapid stress changes (0.25 MPa/s), the fiber optic sensor measures with higher accuracy the strains of high performance concrete than the vibrating wire strain gauge.Key words: high performance concrete, sensor, vibrating wire, strain, extensometer, Fabry-Perot, fiber optic, instrumentation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".