Damage Identification for Beam-Like Structures Based on Physics-Informed Neural Networks

Damage in a beam-like structure due to decreases in its stiffness and/or mass can cause local anomalies in its flexural guided wavefield at locations of damage. Usually, these local anomalies can be intensified by data-driven techniques such as the continuous wavelet transform, the gapped smoothing method, etc. However, the physics of beam-like structures are not considered in these data-driven techniques, leading to a lack of physical consistency in the damage identification results. In this paper, a baseline-free damage identification method is proposed to extract these local anomalies under the assumption that the pristine beam-like structures are homogeneous and isotropic. Flexural guided wavefield of a damaged beam-like structure is used to build a pseudo-pristine model of the beam-like structure by using physics-informed neural networks. When the pseudo-pristine model is built, the prediction of flexural guided wavefield can be generated, and those local anomalies can be approximated by the difference between the prediction of flexural guided wavefield and the corresponding measured flexural guided wavefield. The difference is used to yield an accumulative two-dimensional damage index for further damage identification. Effectiveness and noise-robustness of the proposed method are investigated in a numerical example. Results show that the proposed method is effective and noise-robust in identifying the location and extent of the damage.