Coplanar capacitance sensors for detecting water intrusion in composite structures
Bibliographic record
Abstract
Composite materials are becoming more affordable and widely used for retrofitting, rehabilitating and repairing reinforced concrete structures designed and constructed under older specifications. However, the mechanical properties and long-term durability of composite materials may degrade severely in the presence of water intrusion. This study presents a new non-destructive evaluation (NDE) technique for detecting the water intrusion in composite structures by evaluating the dielectric properties of different composite system constituent materials. The variation in the dielectric signatures was employed to design a coplanar capacitance sensor with high sensitivity to detect such defects. An analytical model was used to study the effect of the sensor geometry on the output signal and to optimize sensor design. A finite element model was developed to validate analytical results and to evaluate other sensor design-related parameters. Experimental testing of a concrete specimen wrapped with composite laminate and containing a series of pre-induced water intrusion defects was conducted in order to validate the concept of the new technique. Experimental data showed excellent agreement with the finite element model predictions and confirmed sensor performance.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".