Wave Based Damage Detection in Solid Structures Using Artificial Neural Networks

Abstract

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented work here is based on Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The CNN model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods.

Machine intelligence in dynamical systems: \A state‐of‐art review

“…The predictions from trained DL models are compared against conventional ML models on a feature engineering dataset to establish the model. He et al (2020) and Wuttke et al (2021) also developed CNN models for wavefield pattern recognition and for fault diagnosis of rotating machinery cross working conditions, respectively. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.…”