## 5 Results and Discussion
### ANN Inputs Relative Importance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{6}{c|}{**Direct Independent Inputs**} & \multicolumn{6}{c}{**Assistance Input Parameters**} \\ \cline{2-11}  & \(a\) & **1** & **L** & **W** & **A\({}_{\text{sf}}\)** & \(\sigma_{\text{y}}\)** & **Config ID** & \(\beta_{\text{P-config}}\) & \(\beta_{a}\) & \(\beta_{f}\) & \(\sigma_{\text{LU}}\) \\ \hline
**Traditional** & 2 & 4 & 1 & 7 & 6 & 5 & 3 & - & - & - & - \\ \hline
**Assisted 1** & 1 & 4 & 2 & 6 & 8 & 5 & 7 & 3 & - & - & - \\ \hline
**Assisted 2** & 1 & 6 & 2 & 7 & 8 & 4 & 5 & - & 3 & - & - \\ \hline
**Assisted 3** & 3 & 5 & 4 & 9 & 7 & 6 & 8 & - & 1 & 2 & - \\ \hline
**Assisted 4** & 8 & 4 & 2 & 5 & 6 & 7 & 3 & - & - & - & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ranking of the different ANN inputs based on their importance (relative weight) for the full size training datasets (results are based on averages for the 80 random combinations of the training dataset). Figure 8: Normalized relative weights of the different ANN inputs for the traditional and assisted approach at the different training dataset sizes (results are based on averages for the 80 random combinations of the training dataset). Finally, it is probably worth mentioning here that when using the traditional approach, accurate predictions can be made only for panels of the exact same configurations used in training. On the contrary, adopting the proposed assisted approach can help in extending the applicability of the ANN model to panel configurations different from those used in training. If we consider for instance the one-bay stiffened panels for which 21 data points are available (panel IDs S-1 to S-21), and all of them have the same spacing between the stiffeners (305 mm). In fact, several parameters related to the stiffeners (such as the stiffeners' cross-sectional area and spacing, fasteners' stiffness and spacing, etc.) will affect the residual strength. If all of these parameters are to be included as inputs for the ANN, at least tens of training datasets (if not hundreds) will be required for the network to capture the effects of all these input parameters on the output. In contrast, in the assisted approach, a single input parameter (\(\beta_{\text{s}}\)), which combines the effect of all of these individual parameters on the SIF, can be included as an assistance input parameter. As such, the ANN will be able to better recognize the effect of the stiffeners and make accurate predictions without the need for a huge training dataset. In addition, the network will also be able to predict the residual strength for stiffened panels even if the spacing between the stiffeners is different from what is used in the training dataset since the assistance parameter \(\beta_{\text{s}}\) completely accounts for the geometry of the stiffeners. The same idea is also true for the other higher-level SIF geometric correction factors being used here as assistance parameters. For instance, the lead-crack overall correction factor (\(\beta_{\text{a}}\)), which is used at assistance level 2, combines different effects together. For a stiffened panel, \(\beta_{\text{a}}\) combines the stiffeners effect, the panel width effect, and the interaction effect of the MSD cracks on the lead crack. Accordingly, the inclusion of this assistance parameter among the inputs makes it easier for the ANN to recognize the complex interactions between the different geometric input parameters. Therefore, it further improves the residual strength predictions' accuracy.