A neat approach to structural health monitoring

In the current paper, an application of the neuroevolution of augmenting topologies (NEAT) algorithm is considered in a structural health monitoring (SHM) application. The algorithm is a variation of genetic algorithms, applied in neural networks, and has the goal of optimising both the topology and the weights and biases of a neural network model. The algorithm is applied here to an SHM problem instead of using feedforward neural networks. The algorithm is called to search for the best-fitting topology in the task, which would otherwise be sought through experimenting with the size and number of the layers of the neural network. Having used the algorithm, the accuracy is found to be close to the one achieved using classically trained neural networks. Another aspect of the application is that subnetworks were defined for every damage case of the problem, whose topologies are much simpler than a fully-connected feedforward neural network. These subnetworks define classification submodels that may be used in different combinations, building models for a subset of damage cases and input features.