Damage identification using convolutional neural networks from instantaneous displacement measurements via image processing
Lucas H. G. Resende, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa, Hernán Garrido, Alexandre Cury, Martín Domizio
Abstract
This work investigates the effectiveness of using convolutional neural networks (CNNs) and instantaneous displacement measurements for damage identification in beams. The study involves subjecting laboratory beams to eight distinct damage scenarios and capturing the vertical positions of 60 points along the beam length during free-vibration tests using a high-speed camera. The data obtained was subsequently used to train a CNN in a supervised manner to estimate the level of damage at each point. Results showed that the CNN models were able to correctly localize and quantify the damage levels when trained on data from all damage scenarios. The soundness of the proposed methodology was demonstrated in a robustness assessment, where all eight damage scenarios were correctly identified even when two of them were excluded from the training dataset.