Modelling dielectric-constant values of concrete: an aid to shielding effectiveness prediction and ground-penetrating radar wave technique interpretation
Bibliographic record
Abstract
Abstract A number of efficient and diverse mathematical methods have been used to model electromagnetic wave propagation. Each of these methods possesses a set of key elements which eases its understanding. However, the modelling of the propagation in concrete becomes impossible without modelling its electrical properties. In addition to experimental measurements; material theoretical and empirical models can be useful to investigate the behaviour of concrete's electrical properties with respect to frequency, moisture content (MC) or other factors. These models can be used in different fields of civil engineering such as (1) electromagnetic compatibility which predicts the shielding effectiveness (SE) of a concrete structure against external electromagnetic waves and (2) in non-destructive testing to predict the radar wave reflected on a concrete slab. This paper presents a comparison between the Jonscher model and the Debye models which is suitable to represent the dielectric properties of concrete, although dielectric and conduction losses are taken into consideration in these models. The Jonscher model gives values of permittivity, SE and radar wave reflected in a very good agreement with those given by experimental measurements and this for different MCs. Compared with other models, the Jonscher model is very effective and is the most appropriate to represent the electric properties of concrete.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".