## 2 Materials and Methods
### Experimental Procedure  
A normalisation technique was applied to all variables before the design of the model to convert them to a similar scale. This is a crucial preliminary step to eliminate potential issues caused by different scales, preventing variables with larger scales from dominating, and ensuring that all variables contribute equally to the model. Standard RML, RML with interactions, and quadratic RM were the models calculated, as the relationships between the various predictors and the dependent variable were not known beforehand. The ability to generalise the obtained model was ensured through a resampling procedure. First, the model's performance was evaluated using test data. At the beginning of the experiment, the dataset was randomly split into two groups: the training dataset and the test dataset. The training or design dataset was used to estimate the model parameters, while the test dataset was used to evaluate the error of each model on unseen data, simulating the model's practical performance. In addition to this, to avoid potential bias from a specific dataset split, both two-fold and five-fold cross-validation techniques were used. In two-fold cross-validation, the model is trained twice, each time with a different half of the data, using the other half for testing. In five-fold cross-validation, 4/5 of the data are used for training, leaving 1/5 for testing, with each fold acting as a different test set in rotation. Both techniques were employed to assess the robustness of the models. Moreover, five-fold cross-validation provides a more accurate and stable estimation of the generalisation error, reducing variance. This process was repeated 20 times to ensure the quality of the results. The mean squared error (MSE), and the correlation coefficient (\(r\)) between FEM and multiple regression models were selected as metrics to evaluate the models. This resampling procedure has been successfully applied by authors in different applications [22; 23]. The correlation coefficient allows us to compare different variables. It is useful to check whether a model adequately estimates the observed variable. That is, to compare what is observed with what is predicted by the model (see Equation (8)). The mean squared error (_MSE_) of an estimator measures the average of the squares of the errors for test sets not used in training stages (see Equation (9)). \[r=\frac{\big{(}t_{i}-\overline{t}\big{)}\Big{(}f_{i}-\overline{f}\Big{)}}{(N-1 )\sigma_{(t_{i}-\overline{t})}\sigma_{(f_{i}-\overline{t})}} \tag{8}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}  
Figure 5: **(a,b)** Different views of stress calculation by FEM. As expected, the maximum stress is observed at the tip of the stiffener, (**c**) deformation calculations by FEM. Additionally, when using multiple regression, it is possible to determine which variables are the most relevant by observing the \(p\)-values of the coefficient t-tests. The closer the \(p\)-values of a coefficient are to zero, the greater the statistical accuracy of that variable influencing the regression. Therefore, that variable affects the relationship sought between inputs and output. The amount and direction of the influence is measured analytically by the coefficient computed for that variable. It is important to note that the most relevant variables have been highlighted in bold in Table 6. Above all, the parameter P3 (leg web plate thickness) is the most decisive, together with its combinations with P4 and P5 and its quadratic value with which a better fit between the multiple regression model and the FEM model is achieved. From a structural point of view, there exist parameters that have weak weights in the regression equation, but this subtle contribution can affect the appearance of cracks in the study zone. For example, the parameter P2 (distance to the diaphragm), although it does not contribute, is very important and it is mandatory not to reduce it to less than 50 mm, or a collapse could occur.