## 5 Results and Discussion
### Assisted-ANN Performance for Different Learning Algorithms
This observation is rather interesting where it indicates that the accuracy improvement attained using the assisted approach becomes more pronounced when the original error of the traditional approach is higher. It is probably worth to mention here that the computer CPU time required to train and optimize the ANN using these three learning algorithms differs significantly. In terms of the run time, the SGC algorithm is the fastest while the BR is the slowest. For comparison purposes and to get a sense of the computational burden difference, taking the SCG as a reference, the relative run times for LM and BR are \(2\times\) and \(104\times\), respectively. The results presented in Table 2 and Figure 3 are averaged values for the 80 random combinations of the testing datasets. However, the calculated performance metrics are somewhat different for each of the 80 testing datasets combinations. To get a closer look at the results obtained using the individual datasets, Figure 4 shows exemplary results for one of the 80 testing datasets combinations (it contains 19 data points, and the sample ID for each data point is indicated in the figure). The upper part of Figure 4 shows the experimental residual strength value for each sample alongside with the ANN predictions obtained using the traditional approach and assisted 3 and 4 scenarios (assisted 1 and 2 are \begin{table}
\begin{tabular}{c c c c|c c c|c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c|}{**BR**} & \multicolumn{3}{c|}{**LM**} & \multicolumn{3}{c}{**SGC**} \\ \cline{2-10}  & \(\mathbf{MAE_{P}}\) & \(\mathbf{RSME_{P}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{MAE_{P}}\) & \(\mathbf{RSME_{P}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{MAE_{P}}\) & \(\mathbf{RSME_{P}}\) & \(\mathbf{R^{2}}\) \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline
**Traditional** & 3.8 & 5.51 & 92.39 & 5.59 & 7.89 & 81.19 & 7.65 & 10.33 & 75.94 \\ \hline
**Assisted 1** & 3.76 & 5.42 & 92.61 & 5.56 & 7.92 & 84.56 & 6.41 & 8.67 & 83.32 \\ \hline
**Assisted 2** & 3.45 & 5.04 & 93.16 & 4.86 & 6.8 & 87.89 & 6.05 & 8.16 & 84.49 \\ \hline
**Assisted 3** & 3.13 & 4.26 & 95.14 & 4.42 & 6.17 & 89.85 & 5.52 & 7.22 & 87.96 \\ \hline
**Assisted 4** & 2.97 & 4.07 & 96.21 & 3.31 & 4.2 & 96.04 & 4.13 & 5.48 & 92.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics for each learning algorithm using the traditional and assisted ANN modeling approaches (averages for the 80 random combinations of the testing dataset). Figure 3: The effect of the assisted approach on the ANN prediction accuracy using the different learning algorithms. The \(\mathrm{MAE_{P}}\) values for the traditional and assisted approaches residual strength predictions (**top**), together with the error reduction of the assisted approach relative to the traditional approach (**bottom**). Averaged results for the 80 random combinations of the testing dataset. not shown in order not to congest the figure). The lower part of the figure shows the magnitude of error in the predicted residual strength using the traditional and the two assisted scenarios. The figure shows that the ANN residual strength predictions obtained using the two assisted scenarios are superior to the predictions obtained using the traditional approach for almost all the shown samples.