## 6 Comparison of the Models  
The network allows us to estimate the strength based on the material composition of the floor mortar and the compressive strength value determined by the Schmidt hammer. This allows the use of a nondestructive method to evaluate the peel strength, thereby not exposing the tested surface to the repairs necessary after using the pull-off method. Subsequently, using the Schmidt hammer, it is possible to obtain both the compressive strength and pull-off strength. These are the two most important characteristics of floor substrates. The machine-learning algorithms used in the article are accurate in predicting the subsurface tensile strength of green cementitious composites. This can be concluded from the values of the parameters determining their accuracy, which were \(R^{2}\), \(NRMSE\), \(MAE\), and \(MAPE\). Figures 15-19 and Table 4 compare the above algorithms. It can be seen that all the models tested predict the tensile strength of green cement composites well. The BFGS algorithm with one layer of hidden neurons performed best. However, since none of the models were perfectly accurate (\(R^{2}\) would be equal to 1; and \(NRMSE\), \(MAE\), \(MAPE\), and relative error would be equal to 0), there is still room for improvement in the algorithms by building other databases or using other algorithms. Figure 14: Relationship between the predicted value and the experimental value of the pull-off strength \(f_{h}\) for ANN test, train, and validation process for the CG algorithm with two layers of hidden neurons. Figure 16: Comparison of models for predicting the pull-off strength of the surface layer \(f_{h}\) due to the value of _NRMSE_. Figure 17: Comparison of models for predicting the pull-off strength of the surface layer \(f_{h}\) due to the value of _MAE_. Figure 15: Comparison of models for predicting the pull-off strength of the surface layer \(f_{h}\) due to the value of \(R^{2}\). The network based on the BFGS algorithm with nine hidden-layer neurons performed best in predicting the pull-off strength of cement floor underlayments. It obtained the following network fit evaluation indices: \(R^{2}=0.8399\), \(RMSE=0.1637\), \(MAE=0.1290\), and \(MAPE=9.8658\) %. According to the article [10], the values of determination coefficients \(R^{2}\) achieved by the selected network are within the range of a very good match with the results obtained experimentally because \(R^{2}>0.81\). The MAPE absolute percentage error \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Learning Algorithm**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{\(R^{2}\)**} & \multirow{2}{*}{\(NRMSE\)**} & \multirow{2}{*}{\(MAE\)**} & \multirow{2}{*}{\(MAPE\)**} \\  & **Hidden Layers** & **Hidden Neurons** & & & & & \\ \hline gradient descent & 1 & 2 & 0.7807 & 0.1408 & 0.1460 & 11.47 \\ gradient descent & 2 & 13–15 & 0.7386 & 0.1262 & 0.1582 & 11.87 \\ conjugate gradient & 1 & 3 & 0.8134 & 0.1109 & 0.1391 & 10.32 \\ conjugate gradient & 2 & 11–8 & 0.7734 & 0.1332 & 0.1488 & 10.87 \\ Broydem–Fletcher– & 1 & 9 & 0.8399 & 0.1028 & 0.1290 & 9.87 \\ Goldfarb–Shanno & 2 & 4–3 & 0.7657 & 0.1382 & 0.1507 & 11.35 \\ Levenberg–Marquardt & 1 & 7 & 0.7934 & 0.1261 & 0.1410 & 10.41 \\ Levenberg–Marquardt & 2 & 4–10 & 0.8046 & 0.1112 & 0.1481 & 11.02 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of accuracy of compared neural networks. Figure 19: Comparison of models for predicting the pull-off strength of the surface layer \(f_{h}\) due to the value of average relative error. Figure 18: Comparison of models for predicting the pull-off strength of the surface layer \(f_{h}\) due to the value of _MAPE_. values of up to 10% are defined as a good fit. As can be seen in Figure 6, the presented network correctly predicts the pull-off strength of the surface layer. The strength ranging from 0.8 MPa to 2.6 MPa is predicted with similar accuracy. The result obtained for the experimental strength above 3 MPa significantly deviates from the rest of the results.