## 4 Prediction  
Current methods of determining abrasion resistance share similar disadvantages, including damaging sample surfaces and time-consuming testing. The possible transformation of some tests into prediction methods and, therefore, the optimization can be achieved by the ANN and RF. This can contribute to reducing the amount of destructive test methods in favor of prediction and, therefore, produce a sustainable approach. Created prediction algorithms were based on selected mortar components with the cycles of testing and mass of a sample after drying (Table 4). The output was assumed as the depth of wear. Water and sand were omitted, as the content for all samples stays the same. The total database consisted of 88 cases. One has to remember that the abrasion resistance of cementitious composites depends on several factors, including paste hardness, aggregate hardness, aggregate/paste bond, and compressive strength. Even when having perfect ingredients, other factors such as curing and surface finishing techniques can \"spoil\" the end result of abrasion resistance. Machine learning algorithms should not be treated as infallible tools. However, they can be helpful in terms of choosing amounts of ingredients to obtain desired abrasion resistance properties. It is recommended that the final mortar quality should be verified with additional methods, such as non-destructive tests (NDT). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Series**} & \multirow{2}{*}{**Cement [-]**} & \multirow{2}{*}{**Fly Ash [-]**} & \multicolumn{2}{c}{**Granite**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Mass of the**} & \multirow{2}{*}{**Depth of**} \\  & & & & & **Powder [-]** & & \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Selected input and output parameters. _Statistical Analysis_  
Choosing the \"right\" inputs is a very important issue regarding the artificial neural network. In order to verify parameter chosen in the previous paragraph, statistical analysis \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & **Series** & **Cement [-]** & **Fly Ash [-]** & \begin{tabular}{c} **Granite** \\ **Powder [-]** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Cycles** \\ \end{tabular} & \
\begin{tabular}{c} **Mass of the** \\ **Sample [g]** \\ \end{tabular} &
\begin{tabular}{c} **Depth of** \\ **Wear [mm]** \\ \end{tabular} \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._was performed to establish whether samples came from a normally distributed population. This includes histograms (Figure 8) with a mean average (x\({}_{i}\)), standard deviation (\(\sigma\)), and the Shapiro-Wilk test. For a chosen alpha level and significance \(\alpha=0.01\), according to the null hypothesis of the Shapiro-Wilk test, if the \(p\)-value is less than the alpha level, the null hypothesis is rejected and the data tested are not normally distributed. If the \(p\)-value is greater than the chosen alpha level, the null hypothesis that the data came from a normally distributed population cannot be rejected [30]. The results of the Shapiro-Wilk test are presented in Table 5.  
Based on these analyses, the most efficient ANN was the one with six input variables, thirteen neurons of one hidden layer, logistic activation functions of the layers (hidden and output), and the quasi-Newton learning algorithm. Using the random forest algorithm, the most efficient was the one with six input variables and two hundred trees. The results of the prediction of the depth of wear using a neural network and random forest are presented in Figure 9.  
Figure 8: Histograms with mean average (\(\bar{x_{i}}\)) and standard deviation (\(\sigma\)) for (**a**) cement, (**b**) fly ash, (**c**) granite powder, (**d**) cycle of testing, (**e**) mass of the specimen, and (**f**) the depth of wear. The results (Figure 9) were presented using the ANN and random forest.