## 3 Results and Discussion  
As stated, two machine learning algorithms were used to predict open porosity for an industrial-cement-based mortar. According to the data, 1274 samples were used for the simulation. Table 1 (substrate characterization) and Table 2 (applied mortar characterization) show the mean and standard deviation separated by variables and characteristics to assist in visualizing and understanding the database used. The results indicate that the characterization values for ceramic substrates (HCB and SCB) and concrete substrates (CS and CB) are similar in magnitude. In contrast, the lightweight concrete block (LCB) shows approximately 60% lower bulk density and 29% higher open porosity than the other concrete substrates. This pattern observed in the substrate characterization is also reflected in the applied mortar. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Substrates**} & \multicolumn{1}{c}{**Bulk Density (kg/m3)**} & \multicolumn{1}{c}{**Open Porosity (\%)**} & \multicolumn{1}{c}{**Aw (kg/m2-s0.5)**} \\ \hline \multirow{6}{*}{Mean} & CS & 2224 \(\pm\) 9 & 11.5 \(\pm\) 0.4 & 0.023 \(\pm\) 0.003 \\  & CB & 2113 \(\pm\) 21 & 14.5 \(\pm\) 1.0 & 0.332 \(\pm\) 0.003 \\  & LCB & 1319 \(\pm\) 72 & 16.8 \(\pm\) 1.4 & 0.308 \(\pm\) 0.021 \\  & HCB & 2071 \(\pm\) 13 & 16.5 \(\pm\) 1.5 & 0.037 \(\pm\) 0.004 \\  & SCB & 2059 \(\pm\) 2 & 18.3 \(\pm\) 0.3 & 0.104 \(\pm\) 0.023 \\ \hline \multirow{6}{*}{Min/Max} & CS & 2216/2241 & 10.9/11.8 & 0.013/0.026 \\  & CB & 2084/2138 & 13.3/15.6 & 0.326/0.336 \\ \cline{1-1}  & LCB & 1194/1401 & 15.1/18.3 & 0.289/0.348 \\ \cline{1-1}  & HCB & 2051/2082 & 15.1/18.7 & 0.031/0.042 \\ \cline{1-1}  & SCB & 2002/2096 & 17.0/19.3 & 0.066/0.134 \\ \hline \hline \end{tabular} CS—substrate concrete slab; CB—concrete block; LCB—lightweight concrete block; SCB—solid ceramic brick; HCB—hollow ceramic brick; \(\pm\)standard deviation.  
\end{table} 
Table 1: Substrate characterization. Figure 3: Machine learning workflow on Orange Software. Concerning constructing the machine learning models, eightly percent of the data was used to train the models, and twenty percent was used to predict the mortar's open porosity. The widget Data Sampler randomly divided the data. The widget Test and Score was used to evaluate the training set using a cross-validation technique, and the Predictions widget was used to evaluate the algorithm performance. Table 3 shows the training results. The parameters used in the SVM configuration were as follows: cost (C) = 1.00; and regression loss epsilon = 0.10, Kernel linear. For RF, they were as follows: number of trees = 10; and number of attributes considered at each split = 6. One of the disadvantages of using low-code software is the limitation of parameter configuration. However, the possibility of testing different algorithms with extreme simplicity reduces the time it takes to design the model. The database was randomly divided into the indicated proportion ten times to construct the evaluation metrics for testing the models. Table 4 shows the results and the mean value for SVM, and Table 5 shows the same for RF. The R\({}^{2}\) of the RF and SVM models were generally almost the same (0.904 and 0.908 for the training data, respectively). The SVM performance was also slightly better than that of RF for the test data (0.896 and 0.880, respectively). The Feature Importance widget was used to evaluate each feature's contribution to the model. This widget explains regression models using a trained model and reference data as the input; in addition, the method calculates each feature's contribution to the prediction by assessing the increase in the model's prediction error after permuting the feature's values, thereby disrupting the relationship between the feature and the target [23]. Figures 4 and 5 show the results for the RF and SVM algorithms, where the bar indicates the mean with the standard deviation after five permutations.