Detection of Ungrouted Cells in Concrete Masonry Constructions Using a Dielectric Variation Approach
Bibliographic record
Abstract
In this study, a new technique for detecting ungrouted cells in concrete block masonry constructions was developed. The concept, based on detecting the local dielectric permittivity variations, was employed to design coplanar capacitance sensors with high sensitivities to detect such construction defects. An analytical model and finite element simulations were used to assess the influence of the sensor geometrical parameters on the sensor signals and to optimize the sensor design. To experimentally verify the model, the dielectric properties of various materials involved in concrete masonry walls were measured. In addition, a masonry wall containing predetermined grouted and ungrouted cells was constructed and inspected using the developed sensors in a laboratory setting. Moreover, different capacitance sensors were designed and compared with respect to their sensitivity, signal-to-noise ratio, and coefficient of variation of the inspected measurements. Excellent agreements were found between the experimental capacitance signal response parameters and those predicted by the analytical and finite element models. The proposed sensor design, coupled with a commercially available portable capacitance meter, would facilitate employing this technique in the field for rapid inspection of masonry structures without the need for sophisticated data analyses usually required by other more expensive and time consuming methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".