  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.
