## 5 Conclusions  
This research aims to classify the type of three rocks and concrete based on the recorded vibration signal obtained by rotary drilling in an experimental laboratory, which has been achieved. This research follows the authors' efforts [43] to classify the rock based on a vibration signal from 2014--when a cumulative periodogram was used as a classification method. The reliability of that classification method was poor, about 40%. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Classification Rock** & **Granite** & **Limestone** \\ \hline A. & Decision rule & \begin{tabular}{c} (_Count Peaks\_10\geq 527) \(\cap\) \\ (_Sum\_of\_abs\leq 15,277_) \(\cap\) (_Fluctuation_\(\leq 132\)) \\ \end{tabular} & \begin{tabular}{c} (_Count Peaks\_10\geq 527_) \(\cap\) \\ (_Sum\_of\_abs\leq 15,277_) \(\cap\) (_Fluctuation_\(\leq 132\)) \\ \end{tabular} \\ \hline B. & Decision tree & \begin{tabular}{c} Sum\_of\_abs\>15,277 \\ the expected rock in the test set \\ and their classification. \\ \end{tabular} & \begin{tabular}{c} Sum\_of\_abs\<15,277 \\ (100,005) \\ \end{tabular} & \
\begin{tabular}{c} Sum\_of\_abs\<10,522 \\ (100,005) \\ \end{tabular} \\ \hline C. & Table evaluating the reliability &
\begin{tabular}{c} \(\text{classified}\) \\ of the classification \\ \end{tabular} & 0 & 1 & Classified & 0 & 1 \\ \cline{3-5}  & 0 & 72 & 0 & 0 & 76 & 0 \\ \cline{3-5}  & 1 & 0 & 27 & 1 & 5 & 18 \\ \hline D. & Reliability of classification & \multicolumn{3}{c}{\(99/99=100\%\)} & \multicolumn{3}{c}{\(94/99=95\%\)} \\ \hline E. & The success of classification & \multicolumn{3}{c}{Excellent} & Not good \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The new classification of limestone via the AdaBoost method. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{real} \\ \cline{2-4} Table evaluating the reliability of the classification &
\begin{tabular}{c} Classified \\ \end{tabular} & 0 & 1 \\ \cline{2-4}  & 0 & 76 & 0 \\ \cline{2-4}  & 1 & 5 & 18 \\ \hline Reliability of classification & \multicolumn{3}{c}{95/99 = 96\%} \\ \hline The success of classification & \multicolumn{3}{c}{Not good} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification for the rocks granite and limestone. We propose a novel method for rock classification based on the analysis of the vibration signal generated during drilling. This method can classify rocks quickly, taking only about one quarter of a second for recording and a few milliseconds for evaluation. It does not require complex equipment or sensors, as it uses an existing drill, a microphone, and a computing device. The solution includes two tasks. For classification purposes, it was first necessary to select the length of the vibration signal and such attributes of the vibration signal that have statistically significantly different statistics for the used materials. Several attributes of the vibration signal were calculated for a time series of 500-length and 1500-length vibration signal data. A cluster dendrogram, an ANOVA test, and a boxplot were used to determine the signal's useful attributes and correct length. A time series of 4500 data measured in 1/4 s was proposed as a suitable length of the vibration signal. Vibration signal attributes were calculated for three time series of 1500 data lengths. The attribute used for classification was determined as the minimum of the three calculated. Suggested attributes of the vibration signal used in the object vector are _Sum_of_abs_, _Count Peaks_10_, _Count Peaks_15_, _Variab_, and _Fluctuation_. The second task consisted of determining and verifying the classification rule. The data were randomly divided into a train set and a test set with a proportion of 80% and 20%. Classification rules were established from train set data using a decision tree machine-learning tool.