## 3 Results
### Parameter Identification
#### 3.2.1 Concrete Cover Thickness  
The thickness of the concrete cover (\(h\)) is a continuous variable that influences almost all attributes, including amplitude (\(A\)), offset (\(O\)), and shape attributes (\(d_{x}\) and \(d_{y}\)). The regression and statistical analysis of the \(h\) parameter are presented in [44]. Figure 20 illustrates the effect of concrete cover thickness on the measurement waveforms for the eddy current method. As a preliminary step in this research, association rules have been extracted. These rules provide a deeper insight into the relationships between structural characteristics and waveform features. The fundamental association rules are listed in Table 5.  
In the next step, the curves representing attribute values as a function of \(h\) are created to determine the optimal set of attributes. Subsequently, the cross-correlation between these curves is assessed. If the correlation value is high and the standard deviations of the individual parameters are comparable, the attributes are combined to minimize noise interference. On the other hand, any attribute with a significantly higher standard deviation is excluded from the attribute set. This method parallels techniques used in rough set theory. Figure 20: EC transducer—waveforms obtained for different concrete cover thicknesses. The investigations (EC method) show a strong correlation between \(d_{\rm x}\) and \(d_{\rm s}\) shape attributes (Figure 17). To mitigate the influence of noise, these attributes are averaged, creating a new shape feature. Before aggregation, the attribute \(d_{\rm x}\) is multiplied by 1.5 to align its values more closely with those of the \(d_{\rm s}\) attribute. Subsequently, the significance of the attributes is assessed first using the InfoGain filter and then through wrappers with the Naive Bayes (NB) classifier. Both attributes (amplitude and the shape attribute \(d_{\rm h}\)) are determined to be significant. The Decision Tree C4.5 algorithm is employed for the final tests. The research in [44] demonstrates the algorithm's effectiveness at identifying reinforced concrete (RC) structure parameters. Table 6 shows the classification accuracy across different cover thicknesses. The high identification accuracy is attributed to a substantial step size of 10 mm. Table 7 illustrates results obtained with a smaller step size of 5 mm. Figure 21 presents the amplitude as a function of \(h\). The studies conducted on the magnetic method showed a strong correlation among the curves representing the amplitudes of all spatial components as a function of \(h\). These functions closely resemble the corresponding curve obtained from the eddy current tests, although the latter exhibited more dynamic variations. Due to the high correlations observed, the three amplitude features are combined to form a new attribute. A strong correlation is also noted among the shape attributes (MFL method). The optimal model identified using wrappers included the attribute \(d_{\rm x}(B_{\rm x})\). Other shape attributes exhibit a high correlation with it. Among these, the attributes \(d_{\rm x}(B_{\rm y})\) and \(d_{\rm x}(B_{\rm z})\), derived from the inflection point (Figure 18), displayed the highest standard deviation. Attributes such as \(O(B_{\rm y})\) and \(O(B_{\rm z})\) could also be incorporated into the model; however, it is impossible to ascertain which model is superior, as all demonstrate 100% efficiency, as indicated in Table 8. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \(h\) (mm) & **20** & **30** & **40** & **50** & **60** & **70** \\ \hline Correctness of the identification (\%) & 100 & 100 & 100 & 100 & 98 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correctness of the concrete cover identification—eddy current method, step of 10 mm.