## 5 Implementation  
An important issue is the question of the implementation of the developed defect detection algorithms and principles. In this paper, all results and conclusions were based on experimental data for homogeneous pile cases. In the experiment, the length and diameter of the piles, and the material and the pouring method, were constant. In fact, in order to implement the obtained results in practical application, it is essential to extend the experiments with variation of other parameters. However, according to a preliminary estimate, these variations will not have a significant effect on the methods used in the algorithm; the accuracy of the ANN classifier and its consistency in defect detection at this stage are beyond doubt. The following are some important recommendations for introducing an ANN classifier when interpreting:  
1. The data obtained should be subjected to preprocessing. The following procedures should be required, according to the authors:  
* The initial conversion operation of the resulting signal is the Fourier transform window. The next step is to work with the frequency spectrum of the signal. It should be pointed out that the equipment used in the experiment already included a function to transform the signal from the time to the frequency domain. It is, therefore, possible to initially take this spectrum as input data for processing. * So-called deep learning should be used, the first of which should be an algorithm that shows whether or not an ANN classifier can be applied to the acquired signal data. It should be mentioned that experiments have shown that, especially in the first steps, an algorithm for estimating amplitudes at the characteristic frequencies of the signal spectrum can be used instead of the ANN classifier. For the experimental data used in this paper, the characteristic frequencies are: 4-6, 27-32, 52-60, 79-87, 90-113. One simplified approach is to compare the average amplitude at the contiguous and the above frequency ranges. An instance where it is smaller is a condition for the application of the ANN classifier. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Experiment** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Defined Classes** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Peaks** \\ \end{tabular} } & \multicolumn{5}{c}{**Accuracy for Different Types of Learning Algorithm, \%**} \\ \cline{4-7}  & & & **Fine Tree** & **Liner SVM** & **Quadratic SVM** & **Fine KNN** & **Medium KNN** \\ \hline
1 & 9 & 5 & 72.5 & **85.9** & 81.9 & 82.6 & 61.7 \\  & 9 & 4 & 66.4 & **75.8** & 75.2 & 73.2 & 60.4 \\  & 9 & 3 & 67.1 & **75.8** & 74.5 & 69.1 & 62.4 \\
3 & 4 & 5 & 73.2 & 71.1 & **91.9** & 87.9 & 71.1 \\  & 4 & 4 & 75.8 & 62.4 & 79.2 & **86.6** & 71.8 \\  & 4 & 3 & 71.8 & 63.1 & **81.9** & 77.9 & 69.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the experiments. * Formation of an input matrix of size (1 \(\times\) 5) for the classifier operation, attaching an array of five points, where each one is the peak of the signal spectrum obtained by low strain integrity testing. It is reasonable to use the first five peaks, which has been proven in the course of this work. 2. In all possible experimental algorithms, the defect-free pile was determined with high confidence. In a practical context, the authors suggest creating an experimental pile to produce such a characteristic. Further, in a second preprocessing step, the authors recommend the use of an algorithm to detect defects in principle from the accumulated external data. It is possible to run dynamic clustering algorithms, find new clusters, and modify the classifier already directly in the device, with the possibility of recognizing defect classes detected by dynamic clustering algorithms. 3. Accumulate history from the data by matching the characteristic points of the signal spectrum with the defects detected.