## 4 Results and Discussion  
Figure 3 shows the appearance of the received signal in the three experiments and the spectra of these signals. The spectrum is obtained using a Fourier transform. The utilized apparatus immediately provides the generation of this spectrum. Therefore, the signal spectrum obtained through the software application of the instrument was used in  
Figure 2: Low strain integrity testing: (1) cast-in-place pile; (2) soil mass; (3) handheld hammer; (4) equipment set; red dotted lines indicate wave propagation. further processing. The signals in Figure 3 were taken from the first transducer in three different experiments. The bottom row of Figure 3 illustrates the results of the same three experiments, superimposed onto each other. The result of the data visual analysis is the idea of characteristic peaks in the spectral signal, taken at different points in the experiment. This idea forms the basis of the processing method proposed in this article. Figure 4 shows the operation of the peak search algorithm (first five) for one of the signals. To generate test data and develop an algorithm, five characteristic points were identified for each obtained signal spectrum. These five points are the very first five peak points of the spectrum. A pre-analysis of the data showed a spread of the number of peaks for each signal, from 7 to 15. For the initial stage, the first five peaks were selected. The number five gives further insight into the effect of the number of peaks selected as an input on the convergence of the algorithm and the quality of the solution obtained. It should be emphasized that an evaluation of the impact of the number of peaks on the performance of the whole algorithm will be described below, once the proposed solution has been selected and proven to be viable. Figure 4: The algorithm operation for searching the peaks (first five) for one of the signals. Figure 3: Appearance of the experimental data (first sensor, three experiments). Working with the data peaks of all available signals, the following patterns were found: most of the peaks were in certain frequency ranges. The distribution between the sensor and finding a peak in a certain frequency range is shown in the Table 1.  
The criterion for range selection was to find more than 80% of the values in the range, and not to increase the percentage of finding when the range is expanded. The information presented in Table 1 will be used to improve the ANN classifier algorithm. Figure 5 shows the result of training an ANN classifier (Confusion matrix) on pre-processed data by Linear SVM. The given ANN classifier was trained on data representing an array of values, where the first five values are the first five peak points of the signal and the sixth value is the class number. The training of the classifier used the membership of each class, where the class was defined as each sensor installed on the pile: Class 1-3: three sensors on defective pile \(N4\); class 4-5: two sensors on defective pile \(N2\); class 6: defect-free pile; class 7-9: defective pile \(N4\). As can be seen from the Confusion matrix results, with an overall accuracy of the ANN classifier (accuracy = 85.9%), class 6 is predicted to be 100% correct. This means that the ANN classifier is 100% accurate in predicting whether there is a defect in the pile. The highest error percentage of 33% is in class 8, and it can be seen that errors occur in false class 7 and 9 instead of class 8. The 7, 8, and 9 sensors are sensors installed on the same defective pile \(N4\). It is probably more appropriate to operate an ANN classifier where all