## 1 Introduction
The authors of [3] provide an extensive review and comparison of mass operators, among them the fitted SWA, the frequency-dependent SWA, and the artificial neural network (ANN). The fitted SWA is the most straightforward method to calculate mass coefficients. It consists of defining the mass coefficients as design variables of a minimization problem or a curve fitting problem [2; 3] where the error \(E\) between the response calculated with the finite element method and the response provided by the mass operator is minimized as follows [3]:  
\[E=\min\Bigl{\{}\frac{1}{2}\Bigl{|}\mathrm{F}_{FEM}(\omega)-\mathrm{F}_{MOP}( \omega)^{2}\Bigr{|}\Bigr{\}}. \tag{1}\]  
However, this method works well only over small frequency ranges with few modes. To solve this issue, the authors of [3] considered a frequency-dependent SWA where the frequency range is split into subranges and a fitted SWA is created independently for each subrange. This method is however not well suited to closely spaced modes. In order to generalize the definition of the mass operator, the authors of [3] presented the use of an artificial neural network (ANN) in two different approaches. The ANN can be used to calculate mass coefficients based on input frequencies and accelerations; this is then a generalization of the frequency-dependent SWA. The ANN can also be used to directly provide the force from accelerations and frequency inputs; this is the most general definition of an operator that can convert measured accelerations into quantities of interest such as forces. Both approaches showed great potential for load estimations [3], but the latter approach has shown many drawbacks especially regarding the ability to generalize the mass operator as an ANN, if the tested structure differs from the analyzed one due to uncertainties such as boundary conditions or material properties. Furthermore, a mass operator as an ANN has not been investigated for the estimation of internal structural stresses. In the last few years, ANNs have shown many successful applications in various domains, from monitoring structural health [7] to predicting tool life [8]. In [9], convolutional ANNs are used to predict vibrations. In a civil structure, vibration-based structural damage can meanwhile be detected using methods based on machine learning [10]. This paper aims to contribute to this expanding field in structural mechanical engineering by expanding the work done in [3] on the use of ANNs. First and foremost, research work was performed on a large-scale structure within an industrial environment. Second, in addition to standard responses such as acceleration response, stresses were successfully predicted. Moreover, several types of neural networks were investigated that could be used to directly convert measured accelerations into structural stresses and hence enable the live prediction of stress during the vibration test. First, the general methods and considered ANNs are presented. Then, a use case is considered in order to test the different ANNs and get a better understanding and confidence about their ability to predict interface loads or stresses in a robust way. Finally, the paper concludes with a discussion on the findings and potential operational use of the proposed approaches.