Modeling of Supplemental Bar-Mounted Fiber Optic Strain Sensor for Structural Health Monitoring Applications
Bibliographic record
Abstract
ABSTRACT Fiber optic sensors have been increasingly utilized in structural health monitoring of large-scale civil structures. Bare fiber sensors are quite brittle, and therefore, their installation and embedment in reinforced concrete elements can be challenging, particularly when using uncommon materials as internal reinforcements in concrete. In the present study, a fiber optic strain sensor is preinstalled on a supplemental bar of adequate length and appropriate diameter. The sensor is attached to a glass fiber-reinforced polymer (GFRP) reinforcing bar in concrete flexural element. Performance under static-loading conditions has been evaluated, and the results have shown potential toward applying the technique to large-scale structures. Another objective of the present study is to develop a numerical model that represents the interaction between the concrete, the reinforcement steel, and the supplemental GFRP rebar, which has the sensor mounted on. The model is calibrated using experimental results. The model can be used to investigate varying parameters including material properties (e.g., compressive strength of concrete), geometrical data (e.g., the length of the supplemental rebar), and loading and boundary conditions, consequently eliminating the need to perform a large number of full-scale costly experiments. The developed model exhibited nearly identical behavior to the experiments after calibration. The study shows that the performance of the present sensing system is primarily affected by the relative sizes of the main and supplemental bars.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".