## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mortar** & **Bulk Density (kg/m3)** & **Open Porosity (\%)** & **Aw (kg/(m2-s0.5)** & **Drying Index** & **CS (MPa)** \\ \hline \multirow{4}{*}{Mean} & MHCB & 1574 \(\pm\) 14 & 22.1 \(\pm\) 0.9 & 0.18 \(\pm\) 0.02 & 0.138 \(\pm\) 0.012 & 4.96 \(\pm\) 0.70 \\  & MSCB & 1570 \(\pm\) 13 & 22.5 \(\pm\) 0.6 & 0.26 \(\pm\) 0.03 & 0.123 \(\pm\) 0.008 & 6.27 \(\pm\) 0.66 \\  & MCP & 1528 \(\pm\) 17 & 25.8 \(\pm\) 1.0 & 0.16 \(\pm\) 0.02 & 0.135 \(\pm\) 0.012 & 3.94 \(\pm\) 0.51 \\  & MCB & 1540 \(\pm\) 23 & 25.3 \(\pm\) 1.3 & 0.17 \(\pm\) 0.03 & 0.155 \(\pm\) 0.014 & 3.97 \(\pm\) 0.44 \\  & MLCB & 1475 \(\pm\) 18 & 30.3 \(\pm\) 0.6 & 0.31 \(\pm\) 0.02 & 0.117 \(\pm\) 0.008 & 3.99 \(\pm\) 0.35 \\ \hline \multirow{4}{*}{Min/Max} & MHCB & 1551/1606 & 20.1/24.0 & 0.13/0.24 & 0.112/0.160 & 3.5/6.4 \\  & MSCB & 1533/1607 & 21.1/23.4 & 0.20/0.32 & 0.104/0.148 & 4.6/7.5 \\ \cline{1-1}  & MCP & 1499/1566 & 23.6/28.3 & 0.13/0.20 & 0.115/0.160 & 2.9/4.9 \\ \cline{1-1}  & MCB & 1496/1590 & 23.3/27.9 & 0.12/0.24 & 0.131/0.189 & 3.1/4.9 \\ \cline{1-1}  & MLCB & 1441/1518 & 29.3/31.5 & 0.26/0.35 & 0.104/0.133 & 3.2/4.7 \\ \hline \hline \end{tabular} MCP—mortar applied to concrete slab; MCB—mortar applied to concrete block; MLCB—mortar applied to lightweight concrete block; MHCB—mortar applied to hollow ceramic brick; MSCB—mortar applied to solid ceramic brick; \(\pm\) standard deviation.  
\end{table} 
Table 2: Mortar characterization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML Models**} & \multicolumn{4}{c}{**Training Set**} \\ \cline{2-5}  & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Support Vector Machine & 0.865 & 0.930 & 0.731 & 0.908 \\ Random Forest & 0.901 & 0.949 & 0.735 & 0.904 \\ \hline MAE (Mean Absolute Error); MSE (Mean Squared Error); RMSE (Root Mean Squared Error); R2 (Coefficient of Determination). & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training results. \begin{table}
\begin{tabular}{c c c c c} \hline
**Runs** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
1 & 1.101 & 1.049 & 0.796 & 0.889 \\
2 & 1.117 & 1.057 & 0.793 & 0.870 \\
3 & 0.954 & 0.771 & 0.587 & 0.942 \\
4 & 0.953 & 0.976 & 0.814 & 0.907 \\
5 & 1.011 & 1.005 & 0.816 & 0.868 \\
6 & 0.810 & 0.900 & 0.690 & 0.909 \\
7 & 0.914 & 0.956 & 0.749 & 0.897 \\
8 & 1.045 & 1.022 & 0.748 & 0.905 \\
9 & 1.121 & 1.059 & 0.814 & 0.880 \\
10 & 0.839 & 0.916 & 0.748 & 0.894 \\ \hline \multicolumn{5}{c}{Mean} & 0.987 \(\pm\) 0.112 & 0.971 \(\pm\) 0.090 & 0.756 \(\pm\) 0.072 & 0.896 \(\pm\) 0.022 \\ \hline \multicolumn{5}{c}{MAE (Mean Absolute Error); MSE (Mean Squared Error); RMSE (Root Mean Squared Error) R\({}^{2}\) (Coefficient of Determination); \(\pm\)standard deviation.} \\ \end{tabular}
\end{table} 
Table 4: Test results for Support Vector Machine (SVM). Figure 4: RF Feature Importance (obtained from Orange software version 3.36.2). \begin{table}
\begin{tabular}{c c c c c} \hline
**Runs** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
1 & 1.080 & 1.039 & 0.834 & 0.891 \\
2 & 0.933 & 0.966 & 0.663 & 0.891 \\
3 & 0.930 & 0.964 & 0.745 & 0.909 \\
4 & 0.845 & 0.919 & 0.734 & 0.918 \\
5 & 1.136 & 1.066 & 0.824 & 0.852 \\
6 & 1.643 & 1.282 & 0.942 & 0.815 \\
7 & 1.082 & 1.040 & 0.840 & 0.878 \\
8 & 1.184 & 1.088 & 0.841 & 0.892 \\
9 & 1.235 & 1.111 & 0.860 & 0.868 \\
10 & 0.942 & 0.971 & 0.769 & 0.881 \\ \hline \multicolumn{5}{c}{Mean} & 1.101 \(\pm\) 0.228 & 1.045 \(\pm\) 0.104 & 0.805 \(\pm\) 0.079 & 0.880 \(\pm\) 0.029 \\ \hline \multicolumn{5}{c}{MAE (Mean Absolute Error); MSE (Mean Squared Error); RMSE (Root Mean Squared Error); R\({}^{2}\) (Coefficient of Determination); \(\pm\)standard deviation.} \\ \end{tabular}
\end{table} 
Table 5: Test results for Random Forest (RF). Both algorithms indicate that the substrate's features (bulk density and open porosity) are the most significant factors influencing the mortar's open porosity. [PERSON] et al.