## 5 Results and Discussion
### ANN Inputs Relative Importance
As a matter of fact, this observation is consistent with the logic behind assigning the assistance parameters for the different assistance levels according to the theoretical definitions of these parameters (as explained in Section 4.2). The increase in the relative weight of the assistance parameters comes at the expense of the relative weights of the direct independent inputs (since the total weight for all inputs is 1). For instance, at assistance level 4, the relative weight of the assistance input parameter (\(\sigma_{\text{LU}}\)) is about 0.7, which leaves only about 0.3 relative weight for all the seven independent inputs. It can also be seen from the figure that although the relative weights of all the direct inputs is reduced as the assistance parameters are added, some of the inputs are clearly affected more than the others. If we, for instance, consider the \"config ID\", which is used to identify the four different test panel configurations being used in this study, it can be seen that the relative weight of this input immediately drops and becomes close to zero for all the assisted approach scenarios. This drop is rather expected since all of the assistance parameters being used here account for the panel configuration more explicitly. When the weight of any of the inputs becomes very close to zero, this means that this parameter can be excluded from the ANN inputs. However, as previously mentioned in Section 4.2, the essence of the assisted approach being proposed here is to add the necessary assistance parameters to the ANN inputs without deleting any of the direct independent inputs. As such, the ANN itself will recognize the importance of each of the inputs and give it its appropriate weight. Indeed, the results presented here show that the ANN is able to do that successfully. Also, the results presented here clearly show that the ANN is able to handle the different inputs without compromising the performance, though some of the inputs (i.e., the assistance parameters) are dependent on other inputs. Table 4 gives the ranking of the different ANN inputs for the traditional and assisted approach scenarios (for the full-size training dataset) based on their relative weights shown in Figure 8. The table shows that the assistance parameters ranking is clearly higher than most of the other direct inputs. For assistance levels 1 and 2, the assistance parameters are ranked third, and they go to the first rank for assistance levels 3 and 4. Other than the assistance parameters, the two most important inputs are the ligament length (L) and the lead crack half-length (_a_). However, it should be noticed that at assistance level 4, the importance of \"_a_\" diminishes (it goes to rank 8) and \"L\" remains as the only significant input (of course after \(\sigma_{\text{LU}}\)). In fact, at this assistance level, the ANN benefits from the Linkup model's ability to capture the general trend of the residual strength, and the ANN basically corrects the Linkup model predictions based on the experimental results. The fact that the most significant input for this assisted scenario is \"L\", means that the ligament length is the main parameter that causes error in the Linkup model predictions (because of inaccurate estimation of the plastic zone size). This observation is actually in agreement with the findings of [PERSON] et al. [30; 44; 45] where the empirical corrections they developed for the Linkup model are a function of the ligament length alone.