## 6 Results and Discussion
### Sensitivity Analysis  
As previously discussed, the accuracy and potential of the proposed ABC-ANN model for the estimation of the \(S_{j,ini}\) and \(M_{n}\) parameters in TSACWs was acknowledged by comparison with the EC3 and KK component-based formulations. In order to investigate in more detail the effect of all the required input parameters, a sensitivity analysis (SA) was carried out for the selected TSACW specimens. The SA reveals how significantly the model output can be affected by changes within input variables. There are two main types of SA, known as \"global\" and \"local\" sensitivity analyses, where the local sensitivity analysis (LSA) concentrates on the local impact of individual input parameters on the overall performance. The global sensitivity analysis (GSA), on the other hand, evaluates the influence of individual input parameters over their entire spatial range and measures the uncertainty of the overall performance (output) caused by input uncertainty, over interaction with other parameters, or also taken individually. Therefore, considering the nature of the complex non-linear behavior and variation of \(S_{j,ini}\) and \(M_{n}\) parameters in the current study, the GSA was selected as much more rational for investigating the impact of input parameters on the overall performance.  
Figure 11: Taylor diagram visualization of model performance, in terms of \(M_{n}\) predictions.  
Amongst GSA methods, a variance-based approach has been primarily considered in past literature for SA. The method provides a specific methodology for defining total and first-order sensitivity indices for each input parameter of the ANN model. Assuming a model in the form \(Y=f\left(X_{1},X_{2},\ldots,X_{k}\right)\), where \(Y\) is scalar, the variance-based technique takes a variance ratio to evaluate the impact of individual parameters using variance decomposition as:  
\[V=\sum_{i=1}^{k}V_{i}+\sum_{i=1}^{k}\sum_{j>i}^{k}V_{ij}+\ldots+V_{1,2,\ldots,k} \tag{19}\]  
where \(V\) is the variance of the ANN model output; \(V_{i}\) is the first-order variance for the input \(X_{i}\); \(V_{ij}\) to \(V_{1,2,\ldots,k}\) correspond to the variance of the interaction of the \(k\) parameters.  
\(V_{i}\) and \(V_{ij}\), which denote the significance of the individual input to the variance of the output, are a function of the conditional anticipation variance:  
\[V_{i}=V_{x_{i}}\big{[}E_{x_{\sim i}}(YX_{i})\big{]} \tag{20}\]  
\[V_{ij}=V_{x_{i}x_{j}}\big{[}E_{x_{\sim i}}(YX_{i},X_{j})\big{]}-V_{i}-V_{j} \tag{21}\]  
where the suffix \(x_{\sim i}\) designates the set of all input variables apart from \(X_{i}\).  
The first-order sensitivity index (\(S_{i}\)) represents the first-order impact of an input \(X_{i}\) on the overall output provided by:  
\[S_{i}=\frac{V_{i}}{V(Y)} \tag{22}\]  
The abovementioned methodology for calculating the first-order sensitivity index was used in this research study. Major results of the SA are presented in Figure 12.  
Apart from the yield strength (\(f_{y}\)) that depends on material properties, the comparative results indicate that the thickness of top flange (\(th_{tc}\)) has the most influence, while the moment inertia ratio of column to beam (\(I_{col}/I_{b}\)) has the least effect on both the output parameters, \(S_{j,ini}\) and \(M_{n}\). The thickness of the bottom flange clear (\(th_{tc}\)) can be classified as the second most influential input variable, especially in terms of maximum moment capacity \(M_{n}\).