## 3 Results
### Initial Measurements
#### 3.1.2 Features Extraction from Measurement Waveforms  
Following the preliminary analysis of the statistics and charts (e.g., Figures 13 and 15), the following conclusions regarding the features were drawn:  
* Unlike the EC waveforms, the magnetic measurements display an offset that can be utilized as a distinct attribute;
* Attributes obtained from the \(B_{\mathrm{x}}\) component (MFL method) and magnitude (EC method) can be determined using the same approach (presented in Figure 5);
* For spatial components \(B_{\mathrm{y}}\) and \(B_{\mathrm{z}}\), the equal intervals in the domain of amplitude method were employed to extract attributes. Minimizing the number of shape attributes while improving their effectiveness is complex. The research on these features is centered around identifying the diameter of reinforcing bars (the most challenging task in this study). Preliminary tests have indicated that the attributes (such as \(d_{30}\)) are ineffective at identifying the rebar diameter. The problem comes from too small differences in the shapes of waveforms obtained from rebars with only a 2 mm diameter variation (basic association rules confirm this observation--see Table 2). Experiments are conducted on four pairs of rebars (including additional measurements) for the EC method, whereas only one pair is tested for the magnetic method. Statistical analyses are performed for four concrete cover thicknesses, ranging from 20 to 50 mm in 10 mm increments. The T25 transducer was used in the EC method experiments. The association rules presented in Table 2 are relatively weak. The spatial resolution of the EC method is significantly better than that of the magnetic method, leading to much higher confidence values. However, even with the biggest transducer, the EC method performs poorly with high concrete cover thicknesses (\(h\)). Consequently, the study area was restricted to a range of \(h\) from 10 to 50 mm for a preliminary test (the typical concrete cover thickness is between 20 and 50 mm). Shape attributes are derived from normalized waveforms. Therefore, those related to high amplitude values (such as \(d_{70}\) and \(d_{90}\)) have  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{Feature} & \multicolumn{4}{c}{**EC method—magnitude waveform**} & \multicolumn{4}{c}{**MFL method—\(B_{\mathrm{x}}\) waveform**} \\ \cline{2-11}  & \(\mathbf{d}_{10}\) & \(\mathbf{d}_{30}\) & \(\mathbf{d}_{50}\) & \(\mathbf{d}_{70}\) & \(\mathbf{d}_{90}\) & \(\mathbf{d}_{10}\) & \(\mathbf{d}_{30}\) & \(\mathbf{d  
low confidence due to the decreasing distance between measurements as they approached the peak (value 1). Additionally, attributes corresponding to low amplitude values (e.g., \(d_{10}\)) yielded worse results because of the low signal-to-noise ratio (SNR) associated with these measurements (noise has a more pronounced effect when the signal is weak). The influence of the concrete cover thickness on the shape attributes is shown in Figure 16.  
The shape attributes show a linear correlation with the thickness of the concrete cover (\(h\)). The results of the T25 transducer demonstrate the lowest standard deviation around \(h=30\) mm. Below \(h=20\) mm, a significant unwanted influence of the excitation coils on the rebar is observed. When for \(h\) higher than 45 mm, the impact of noise becomes a more critical factor. To improve the usability of shape attributes and reduce their number, all shape attributes from \(d_{30}\) to \(d_{70}\) (which demonstrated the lowest standard deviation) are averaged together. This aggregation minimizes the influence of noise and highlights the differences between waveforms. The resulting feature is called \(d_{\mathrm{s}}\). Statistically, \(d_{\mathrm{x}}\) is the most effective shape attribute. Figure 17 illustrates the relationships between values of \(d_{\mathrm{s}}\) and \(d_{\mathrm{x}}\) and the thickness of the concrete cover.