Recent advances in machine learning have enabled powerful strategies for autonomous data-driven dam-age detection and identification in structural systems. This work proposes a novel method for 3D delam-ination identification in sandwich composite structures. Sandwich structures are prone to damage that is difficult to detect, necessitating efficient and complex inspection techniques. In this context, automated structural health monitoring techniques are of great impact and importance in engineering. Such tech-niques use data obtained by sensors to identify damage to the structure. In this paper, a complete methodology for damage (delamination) identification in sandwich composite structures using machine learning is proposed. The damage was parameterized in two different ways: as parametrized two-and three-dimensional ellipses, and it was considered in three different groups: the core, interface, and skin. The modal data, obtained by the finite element method, was used to train several machine learning mod-els in order to classify the location of the damage. In addition, modal dataset was also used to train arti-ficial neural network regression models for damage localization and sizing. Both classification and regression strategies showed substantial results, and the models proved to be robust enough to identify a wide variety of damages. Results showed that classification models correctly identified the damage on the composite skin and incorrectly identified the interface core-skin damage. The regression model proved to be reliable in identifying an approximate location with an average accuracy of 85%. However, damage sizing is still a challenge to predict based only on modal datasets. (c) 2023 Elsevier Ltd. All rights reserved.
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