## 1 Introduction
These techniques, thanks to their robust information fusion and pattern analysis abilities, enable the classification or regression-based diagnosis of damages [21; 22]. ML techniques for SHM are entirely data driven; and for this reason, a large set of data must be provided to describe the structural state comprehensively [23]. [PERSON] and [PERSON] [24] presented a hybrid physics-aided multi-layer feed-forward neural network to improve damage detection under Lamb wave responses in a numerically modelled aluminum thin panel. To localize damage positions around an aluminum flat plate, [PERSON] et al. [25] combined the finite element method and ANN. In this study, the damage index calculated on the 0-order symmetric UGW propagating mode demonstrated its usefulness in differentiating between different damage regions. [PERSON] et al. [26] used ML to recognize the size and shape of damages in a simulated aluminum beam with 17 different damage configurations by the classification of selected features. [PERSON] and [PERSON] [27] tested an Orthogonal Matching Pursuit process combined with a ML algorithm to automate the damage detection process, offering an efficient technique tested on a numerically modelled aluminum panel. [PERSON] et al. [28] used a DL Lamb waves-based algorithm for rapid, accurate, and automated damage position detection by comparing the full wavefield images of a pristine and a damaged state of two identical aluminum plates. [PERSON] et al. [29] proposed a hybrid method that incorporated a DL model with higher order spectral analysis in order to detect fatigue cracks in an aluminum specimen. [PERSON] and [PERSON] [30] explored a broadband Lamb wave DL algorithm for damage localization and quantification on a corroded aluminum plate. [PERSON] et al. [31] presented an automated technique through ultrasonic wave pattern using a deep autoencoder (DAE) for the damage detection and classification through automatic feature extraction with unsupervised clustering. [PERSON] et al. [32] proposed a multilevel Bayesian procedure for the damage assessment in layered composites using through-transmission ultrasonic data; the method was first validated on synthetically generated data (damage hypothesis was, however, based on the uniform reduction in the Young's modulus of several layers at specific area), and then evaluated on real signals from post-impact damage experiments. [PERSON] et al. [33] proposed a ML application for impact localization on an isotropic plate. The authors compared polynomial regression and shallow neural network results, confirming the effectiveness of the ML procedure in detecting and localizing damages under different combinations of training/test sets. A state-of-the-art review of the different data-driven solutions for SHM and damage detection is provided in [34]. Several works dealing with ML can be found in literature also for composites. [PERSON] et al. [35] discussed the use of vibration-based monitoring and machine learning algorithms (MLAs) for anticipating delamination damage in FRP composites. To create a database for the MLAs' training, the authors used a theoretical model of a FRP beam with delamination under vibration. Support Vector Machines (SVM) demonstrated the best prediction performance for both discrete and continuous parameters of delamination location and size among theMLAs tested: Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), and SVM. The study demonstrated the potential to enhance the prediction capability of MLAs in assessing delamination damage and provided evidence for the applicability of SVM for structural health monitoring of delamination damage in FRP composites. [PERSON] and [PERSON] [36] presented an innovative technique for identifying delamination in sandwich composite structures utilizing ML. The method employed sensor data to train an ML network to categorize the damage location and estimate its size. The technique exhibited promising results, achieving an average accuracy of 85% in pinpointing damage location.