## 4 Conclusions  
In this work, a new methodology for structural damage identification was proposed. Some of the main novelties of this method are as follows: (a) Unlike usual procedures, damage was identified based on the latent space representation of a trained deep autoencoder instead of on the measurement of a reconstruction error between the input data and the reconstructed output of the autoencoder. (b) Clusterability of the latent representations emerging from the deep autoencoder around all classes of the original dataset was used for learning. New data points or synthetic data, generated with a variational autoencoder and providing new variants, are encompassed in the classes defined by the original data, contributing to the clustering of all datasets into classes. This unsupervised classification provides features to predict structural health. (c) Three damage indicators, average density, volume of the convex hull, and ellipse overlap, were defined from the latent space representation based on the preservation of the invariance in the input data through the encoding of the AE. The model was illustrated and validated through a test case using actual experimental data obtained on an externally FRP-strengthened RC beam. The records of impedance of this specimen captured with nine different PZT sensors under different levels of loads and, therefore, of damage were used to fully verify the capability of the proposed method. The proposed model does not require costly and huge experimental results. The three indicators proved to be useful in identifying the progressive deterioration of the specimen and to predict the sudden debonding failure at its earliest stages. With regards to future work, different approaches aimed at improving the model should be explored. One of these alternatives concerns the search and implementation of other indicators able to provide greater robustness to the model. This would enrich the set of metrics used for SHM, thus obtaining a more accurate model. More complex neural network architectures and advanced regularization techniques should also be explored to improve the accuracy and robustness of AE and VAE models. Likewise, considering a more global perspective, the developed techniques should be validated with larger and more complex structures. In this way, the generalization and adaptability capacity of the proposed model could be assessed. Its application to large structures will provide a limited amount of data, in line with the limited number of sensors available. In this case, a preliminary study to guide optimal sensor configurations and locations for different classes of large-scale structures will be needed. Furthermore, temperature effects will influence EMI measurements and, therefore, could overwhelm the effect of damage, easily leading to false detections. For this, the proposed method should be reformulated to deal with thermal effects too. Finally, another very important aspect to be explored in the future, which is gaining relevance in the last few years, concerns transfer learning. The ability of methods able to adapt the knowledge and classifier system built on this problem to solve other new scenarios for similar specimens should be addressed. This would be of significant interest for SHM of FRP-strengthened RC beams, since this would minimize costs and time in the monitoring systems. **Author Contributions:** J.M. : conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, visualization, and supervision. J.P.: conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, and visualization. R.P. : conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing--original draft preparation, writing--review and editing, data curation, visualization, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.