Summary
Deep learning algorithms can be employed to monitor and assess the actual conditions of civil and industrial structures before damage can cause detrimental effects on their safety or serviceability. Within this framework, the suitability of deep learning algorithms has been investigated for linear structures, while there is a lack of studies targeted at demonstrating their effectiveness for damage detection in nonlinear dynamical systems. In this perspective, the present study illustrates preliminary results concerning damage detection in a single-degree-of-freedom Duffing oscillator using a 1-dimensional convolutional autoencoder-based neural network architecture. The neural network training is carried out in an unsupervised manner to avoid the need for labeled databases. Numerical simulations demonstrate that the proposed computational strategy allows for the reliable identification of low damage levels in dynamical systems experiencing nonlinear behavior.
