Damage Detection of FRP-Strengthened Concrete Structures Using Capacitance Measurements
Bibliographic record
Abstract
In this study, a new concept for detecting air voids, water intrusion, and glue infiltration damages in fiber-reinforced polymers (FRPs)-strengthened concrete structures was developed. The concept, based on detecting the local dielectric permittivity variations, was employed to design coplanar capacitance sensors (CCSs) to detect such defects. An analytical model was used to introduce the sensor operation theory and analyze the influence of different sensor parameters on the output signals and to optimize sensor design. Two dimensional finite element (FE) simulations were performed to assess the validity of the analytical results and to evaluate other sensor design-related parameters. To experimentally verify the FE model, dielectric properties of various materials involved in FRP-strengthened concrete systems were measured. In addition, two concrete specimens strengthened with FRP laminates and containing preinduced defects were constructed and inspected in a laboratory setting. Good agreement was found between experimental capacitance measurements and those predicated by the FE simulations. The proposed CCS design, coupled with commercially available portable capacitance meters, would facilitate field implementation of the proposed technique for rapid inspection of FRP-strengthened concrete structures without the need for sophisticated data analyses usually required by other more expensive and time consuming methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".